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FACTORS ASSOCIATED WITH CLINICIANS’
RECOMMENDATION FOR RETURN TO WORK IN
PATIENTS WITH WORK-RELATED SHOULDER
AND ELBOW INJURY
By
Farshid Tabloie, MD, FRCSC
A thesis submitted in conformity with the requirements
for the degree of Master of Science
Institute of Medical Science
University of Toronto
© Copyright by Farshid Tabloie, 2013
ii
ABSTRACT
Farshid Tabloie MD FRCSC, Institute of Medical Science, University of Toronto
Master of Science Thesis – 2013
Factors Associated with Clinicians’ Recommendation for Return to Work (RTW) in
Patients with Work-Related Shoulder and Elbow Injury (WRSEI)
Background: RTW after work-related injuries is a multifactorial process. Factors affecting
clinicians to make RTW-recommendations for patients with WRSEI have not been studied in
the literature.
Purpose: We investigated the associations between group of factors chosen from different
domains (Personal/Environmental) and clinicians’ RTW-recommendations for patients with
WRSEI.
Methods: Study design was cross-sectional. Data were collected from self-reported surveys
and clinical charts of 130 adult workers (not working at the time of visit and referred to WSIB-
Shoulder & Elbow Specialty Clinic-Toronto) with chronic (≥6-months) injuries.
Results: Population mean age was 43.5-years. 52% were female. The average time-since-injury
was 20.4-months (45%>12-months). 70% received RTW-recommendations (regular/modified-
job). 30% received a No-RTW-recommendation. 42% had education≥college-level. 18% had
heavy (>20kg) job-demands. Higher MCS-scores had a significant association (p=0.0003) with
clinicians’ RTW-recommendations.
Conclusion: In patients with chronic WRSEI(s), poor general health-status and high disability,
workers with better mental-health were more likely to receive a RTW-recommendation by
clinicians.
iii
ACKNOWLEGEMENT
I would like to acknowledge the efforts of my supervisor, Dr. Robin Richards, and
thank him for his guidance and support over the duration of the Masters program. This
work would not have been accomplished without his expert opinion and constant
feedback.
I would like to extend my appreciation to my thesis committee members Dr.
Dorcas Beaton and Dr. Peter Smith for their helpful comments, criticisms, and broad
expertise. I greatly appreciate their enthusiasm, guidance and valuable suggestions in
preparing this thesis.
I am also grateful to those staff at the Mobility Program - Clinical Research Unit
at St. Michael's Hospital, Shoulder and Elbow Specialty Clinic at Holland Centre and
Division of Orthopaedic Surgery at Sunnybrook Hospital, who facilitated this research
and helped me at different stages of my project as well as Sunnybrook Orthopaedic
Associates for their generous financial support for my Masters program tuition.
I thank my loving and supportive family, my parents and my sister for their
encouragement. Finally I would like to thank my wife and the love of my life, Pooneh.
She has been a pillar of support throughout my studies, and I would not have made it
through without her and I would like to dedicate this work to her.
iv
TABLE OF CONTENTS
ABSTRACT ________________________________________________ii
ACKNOWLEDGEMENTS ___________________________________iii
LIST OF TABLES __________________________________________vii
LIST OF FIGURE, FORM & APPENDICES ___________________viii
ABBREVIATIONS & ACRONYMS____________________________ix
INTRODUCTION ___________________________________________1
BACKGROUND_____________________________________________5
1. Theoretical Framework________________________________5
1.1 Work Functioning Framework________________________5
1.2 Clinical Decision Making____________________________8
2. Research Question & Hypothesis________________________15
METHODS_________________________________________________17
1. Study Design ________________________________________17
2. Inclusion & Exclusion Criteria__________________________17
3. Data Collection_______________________________________18
3.1. Self-Reported Survey ______________________________18
3.2. Clinical Chart Review _____________________________19
v
4. Outcome Variable ____________________________________23
4.1. Clinician’s recommendation for No RTW _______________23
4.2. Clinician’s recommendation for return to modified job______23
4.3. Clinician’s recommendation for return to regular job_______24
5. Potential Predictors___________________________________25
5.1. Personal Factors _________________________________27
5.2. Environmental Factors _____________________________37
5.3. Other Variables __________________________________39
6. Data Management ____________________________________44
7. Analysis ____________________________________________46
7.1. Descriptive Analysis_______________________________46
7.2. Univariate (Unadjusted) Analysis _____________________46
7.3. Test of Multicollinearity____________________________48
7.4. Multivariate Logistic Regression Analysis _______________48
RESULTS__________________________________________________52
1. Descriptive Analysis __________________________________52
2. Univariate (Unadjusted) Analysis _______________________56
2.1. Variable Selection ________________________________56
2.2. Missing Cases in Logistic Regression Analysis ____________58
3. Test of Multicollinearity_______________________________60
vi
4. Multivariate Logistic Regression Analysis ________________61
4.1. Model Selection __________________________________61
4.2. Final Model ____________________________________63
DISCUSSION_______________________________________________65
CONCLUSION _____________________________________________74
FUTURE DIRECTIONS _____________________________________75
BIBLIOGRAPHY ___________________________________________83
vii
LIST OF TABLES
Table 1: Category & Dimension of Predictors (Sanqvist-2009)__________6
Table 2: Category & Dimension of Potential Predictors_______________26
Table 3: Data Comparison - Availability of Outcome Variable _________45
Table 4: Descriptive Statistics - Continuous Variables________________54
Table 5: Descriptive Statistics - Categorical Variables_____________55, 56
Table 6: Unadjusted Analysis of Predictors ________________________57
Table 7: Data Comparison - Availability of Predictors in Final Model ___59
Table 8: Test of Multicollinearity________________________________60
Table 9: Multivariate Logistic Regression - Model Comparison________62
Table 10: Final Logistic Regression Model ________________________64
viii
LIST OF FIGURE, FORM & APPENDICES
Figure 1: Theoretical Framework________________________________14
Form 1: WSIB Shoulder & Elbow Clinic – Data Extraction Form ______20
Appendix 1: REB Approval Letter-University of Toronto_____________79
Appendix 2: REB Approval Letter-Sunnybrook Health Sciences Centre _80
Appendix 3: Permission Letter for Work Functioning Framework ______82
ix
ABBREVIATIONS & ACRONYMS
CPG: Chronic Pain Grade
MCS: Mental Component Summary (of SF-36 Questionnaire)
PCS: Physical Component Summary (of SF-36 Questionnaire)
Quick DASH: Disability of Arm Shoulder & Hand (Quick version - 11 items)
RTW: Return to Work
SF-36: Short Form Questionnaire (36-items)
UEIW: Upper Extremity Injured Worker
VIF: Variation Inflation Factor
WIS: Worker Instability Scale
WLQ: Work Limitation Questionnaire
WRSEI: Work Related Shoulder and Elbow Injury
WSIB: Workplace Safety and Insurance Board
1
INTRODUCTION
Work-related upper extremity disorders are a significant occupational health
problem in the North America[1-3]
and represent a significant proportion of workers’
compensation costs[4-6]
. Musculoskeletal disorders and traumatic injuries of the upper
extremity accounted for approximately 39% of long-term disability costs, and, according
to statistics from Ontario’s Workplace Safety and Insurance Board (WSIB) (which covers
about 60-70% of work places), for about 40% of all lost time claims over the past 10
years in Ontario workplaces[7, 8]
. Work-related injuries have enormous implications for
injured workers, employers, insurers, and health care providers. Upper extremity illness
and injury accounts for about 25% of work absence in the United States[9]
. Although low-
back pain is the leading cause of work absenteeism in Canada and other industrialized
countries[10]
, according to the Statistics Canada[11]
in 2003, upper extremity was the
anatomic region most frequently injured on the job (43.7%). Cole et al. 2005, reported
that in a cohort of Canadian workers with new musculoskeletal repetitive strain injury,
about 37 percent of workers had wrist or hand pain, 20 percent of them were suffering
from shoulder or upper arm pain and about 15 percent reported elbow or lower arm
pain[12]
.
In general most employees, when given the opportunity, would rather return to
work as soon as possible after being off with an illness[13]
. There is a good understanding
that the greatest burden within the insurance system of the worker’s compensation benefit
comes from the small proportion of claims that are longer term[14]
. Less than 8% of cases
2
that are absent from work for more than 6 months due to back pain account for nearly
75% of lost days and medical costs[15, 16]
. Similarly for the work related injuries of the
upper extremity, it has been shown that 6.8% of the claims with a length of disability
greater than one year account for about 60% of the cost and 75% of the total disability
days[17]
. It is quite important for clinicians to identify this subgroup of UEIWs as early as
possible in order to guide them to a suitable treatment plan and provide them with a RTW
recommendation that would meet their abilities and allow a successful return to work,
which in return decreases the cost to the healthcare and insurance system.
Many potential determinants have been identified or studied in order to improve
RTW rate and minimize the duration of absenteeism following injury[18, 19]
. The
promotion of “return to work” (RTW) following occupational injury benefits injured
workers, their families, enterprises and society[18]
. Several studies have documented more
rapid return to work when the patient, health care providers and employers plan return
together. In one study, proactive RTW planning by the physician was associated with a
greater likelihood of RTW during the acute phase (<30 days of disability). In addition,
physician recommendation to return to work was associated with an approximately 60%
higher RTW rate during the subacute/chronic phase (>30 days of disability)[20]
. The
systematic review performed by Franche et al[21]
, showed that early contact between
workplaces and health care providers (from a simple report sent back to the workplace, to
a more extensive visit to the workstation by a healthcare provider) reduced work
disability duration. The role of physicians in returning the injured worker to work is
important enough that the Physician Education Project in Workplace Health as an
3
initiative of the Ontario Medical Association (OMA) section on occupational and
environmental medicine has prepared a practical guide for physicians relating to their role
in the RTW process. According to this guideline the physician is responsible for
communicating with patients, healthcare professionals, relevant authorities such as the
Workplace Safety and Insurance Board (WSIB) and the patient’s employer, case manager
and family (with prior consent from the patient)[22]
.
It is every worker’s goal to return to their pre-illness activity levels unless the
injuries or diseases lead to severe ongoing impairment. However, some patients with
objective evidence of recovery from injury or illness still have subjective complaints
disproportionate to their physical impairment[23]
. According to the study done by
Lindenhovius et al[24]
, there is a substantial discrepancy between objective physical
impairments and perceived disability related to the injured elbow that remains
unexplained. MacDermid et al. also looked at some factors such as grip strength, wrist
range of motion, and manual dexterity to measure physical impairment in patients after
wrist fractures and found that physical impairment accounted for only 25% of their
perceived disability and pain (which was measured by a patient-rated wrist evaluation
form)[25]
. Therefore there might be other unknown factors that contribute to the resolution
of physical impairment such as those related to the worker’s individual capacity,
motivation or the demand of their job.
Since the clinician holds a lot of weight in making RTW recommendations, with a
body of knowledge around the medical condition and its likely course, it is worthwhile
4
considering how the clinician makes this decision. Despite the growing evidence of the
importance of the clinician’s decision-making in influencing RTW outcome[3, 20, 22]
, there
is a paucity of information in the medical literature regarding which factors they consider
when making RTW recommendation for UEIWs. We are not aware of any study that has
examined this issue among workers with a work-related shoulder or elbow injury or
disorder.
The Workplace Safety and Insurance Board (WSIB), Ontario, Canada, is an organization
mandated under the Workplace Safety and Insurance Act to promote health and safety in
Ontario workplace, to facilitate RTW, recovery and Labor Market Re-entry (LMR) of
workers who sustain a work related injury or occupational disease, and to provide
compensation and other benefits where RTW cannot be achieved[26]
. To help promote its
vision in 1998 the WSIB established specialty clinics at various academic health sciences
centers[27, 28]
where workers are assessed and recommendations are made for further
assessment, therapy or RTW. The stated goal of these clinics is to expedite RTW for the
attending injured workers. The focus of this thesis is on one part of that process, which is
the RTW recommendation made by clinicians. Understanding the information gathered
by clinicians and the factors that affects RTW recommendations will allow us to better
understand this important part of the RTW process.
5
BACKGROUND
In this chapter a theoretical framework for clinician’s decision-making on RTW is
introduced and research questions and related hypotheses are described.
1. Theoretical Framework:
The formulation of clinicians’ recommendations for RTW after a work related
injury is a complex and multifactorial process. In order to understand this process better,
the first focus should be on the “return to work” and the factors that are associated with
that. Information from a described work functioning framework and clinical decision
making theories and processes were blended here to describe a theoretical framework of
how clinicians make a decision about RTW in injured workers (Figure 1).
1.1. Work Functioning Framework: Work functioning refers to the efficiency
and appropriateness of an individual’s work performance in relation to a combination of
factors such as personal, environmental and temporal. This has been described as a
conceptual framework by Sandqvist et al. 2004[29]
in different categories of factors and
dimensions of work functioning (Table 1). This framework covers all the potential
predictors that were available to us in this study.
6
Table 1. Summary of Categories of Factors and Dimensions of Work
Functioning Framework as Described by Sandqvist et al. (2004)
Factors Definition
Examples
Personal Factors
Factors that describe the physical
and psychological individual
characteristics which could
impact on their work situation
Age, gender, level of pain, level of disability, education, clinical
diagnosis, occupational demands
Environmental Factors
(related to working life)
Factors in the work environment
that can affect an individual’s
functioning at work
Occupational demands, work duties, support in the workplace,
position in the occupational hierarchy
Environmental Factors
(related to personal life)
Factors in an individual’s
personal environment that can
affect their functioning at work
Social class, support from friends and family, conflict between
work roles and social roles
Dimensions*
Definition
Examples
Work Participation
Level
Ability and opportunity to
accomplish role as a worker and
obtain/maintain a work position in
the society
Related to labor market conditions, financial safety net of the
society, laws and regulations, etc. focuses on the public support
for the individual as well as the demands placed upon workers.
Work Performance
Level
Ability to satisfactorily
accomplish different work
activities and tasks required for a
certain job
It pertains to the worker’s skills during performance while
performing the actual work activities.
Individual Capacity
Level
The physical and psychological
characteristics that enable an
individual to perform work
activities
Worker’s muscle strength or joint motion and their sensibility,
memory, etc. Instead of focusing directly on actual performing
of work activities (unlike the work performance level), deals
with the underlying functions that indirectly affect the
individual’s ability to perform work. *There is no hierarchy between the dimensions and they merely focus on different aspects, or levels of functioning. The assessor must therefore
always collect and interpret results of assessments in all three dimensions[29]
. Due to the interactive relationship between the three dimensions,
a significant change in one dimension could influence one or both of the other dimensions[30]
.
An ideal work assessment should focus simultaneously on all three work
functioning dimensions, which are correlated together but do not have necessarily a
causal relationship. Since it is not clear how a change in one dimension affects the other
dimensions, they should be assessed independently[29]
. Examples of such assessments are
the evaluation of the client’s worker role (society level), observation of the person
7
performing an activity (individual level), or measurement of a work-related pain
condition (body level). This work functioning knowledge is so broad that in order to
obtain this on every patient an integrated teamwork consisting of clinicians, occupational
therapists, social workers and other health practitioners would be required. As Sandqvist
et al. proposed, the different dimensions may be visually understood better if shown as an
inverted cone in which the work participation dimension is wider than the other two
dimensions because it focuses on the person’s interaction with society and not only on
individual factors[29]
. This framework is illustrated in Figure 1.
This framework demonstrates the combination of factors that potentially would be
considered by clinicians from different categories and in different dimensions, when
making RTW recommendation for injured workers. For example it is quite prudent that
clinicians ask injured workers about their occupation and job demands (environmental
factors) while taking clinical history and at the same time if available refer to the job site
analysis report and the demands placed upon patient (work participation/society level),
and/or the functional ability evaluation report (work performance/individual level). It is
also important to include certain measurements such as grip strength in their clinical
examination and review certain parameters such as pain or disability indices (personal
factors) while assessing a) the worker’s physical ability (individual capacity or body
level) and b) the worker’s ability to satisfactorily perform an expected task at work (work
functioning or performance level). This knowledge also helps clinicians to be familiar
with the worker’s social supports and at the same time be aware of the worker’s
individual performance and capacity when performing their clinical assessment in order
8
to formulate a RTW recommendation.
The above describes “WHAT” potential factors clinicians would likely consider
for making RTW recommendations. In the following section, by describing a series of
complex and multilevel strategies known as clinical decision-making process, we attempt
to demonstrate “HOW” these factors might influence RTW recommendations for patients
with work-related shoulder and elbow injury.
1.2. Clinical Decision Making: Clinicians continually integrate vast amounts of
medical information in the clinical setting. They must be thorough yet efficient in
gathering data and use strategies that promote maximal diagnostic proficiency while
limiting costs. In general for any clinical reasoning, both an adequate knowledge base of
medical information and appropriate decision-making skills are necessary to diagnose
and manage medical problems[31]
. Medical inquiry is performed by utilizing the
cognitive and psychomotor skills or techniques to gather medical data and includes
history taking, physical examination, and diagnostic testing. Clinical decision-making
refers to the cognitive processes required to utilize the medical data obtained to evaluate,
diagnose, or manage medical problems[32]
.
Human factor specialists and cognitive scientists have studied the diagnostic and
management decision-making of expert physicians to understand the underlying
processes and to better teach them to novices. Three consistent diagnostic and
management decision-making processes or strategies have been emerged: 1) Pattern
9
recognition, 2) "Rule-using" algorithm and 3) Hypothetico-deductive (information
processing) model[31-34]
.
1.2.1. Pattern recognition: This "skill-based" process corresponds to the
lowest level of the clinical decision-making hierarchy. Without conscious effort,
pattern recognition decision-making is automatic, operates briefly, and processes
information rapidly and in parallel after being activated by sensory input or
conscious thought[31]
.
1.2.2. Rule-using algorithm: Higher on the clinical decision-making
hierarchy is the ability to "use rules", which requires greater understanding than
memorization or pattern recognition alone. It should be noted that pattern
recognition is an essential prerequisite to applying the correct rule. These rules
include heuristics (application of experience-derived knowledge), algorithms, and
clinical pathways[31]
.
1.2.3. Hypothetico-deductive model: Highest in the clinical decision-
making hierarchy is the intellectual ability to make clinical decisions by problem
solving and using previous obtained knowledge to create new solutions
("knowledge based"). The physician must create a unique solution to a clinical
problem by utilizing conscious analytic processing of stored knowledge[31]
. This
approach involves several stages: cue recognition or cue acquisition, hypothesis
generation, cue interpretation and hypothesis evaluation[33]
.
10
Furthermore, a “Naturalistic or Event-driven (the intuitive-humanist) model” has
also been described[33]
. Intuition is the understanding without an overt rationale. It is also
defined as the deliberate application of knowledge, or understanding that is gained
immediately as a whole and that is independently distinct from the usual, linear and
analytical reasoning process. Intuition is a perception of possibilities, meanings and
relationships by way of insight[33]
. This process involves a component of complex
judgment and decision-making in a perplexing, often uncertain situation and synthesizes
empirical, ethical, aesthetic and personal knowledge. Intuitive judgment is used when the
decision is made to act on a sudden awareness of knowledge. This knowledge is related
to our previous experiences and is perceived as a whole[33]
.
It is believed that clinical decision-making is practically based on a
multidimensional model that uses all three bases of decision-making. It contains elements
of the information-processing model but also examines patient specific elements that are
necessary for cue and pattern recognition[35]
. For example, when a patient with work-
related shoulder injury is first seen in the WSIB specialty clinic regardless of the
pathology or physiology of the disease, a combination of facts passes through the
clinician’s mind including the potential signs and symptoms such as pain, limitation in
range of motion, weakness, decreased endurance, etc., as well as factors that would relate
to the type of occupation such as repetitiveness (in cumulative trauma disorders), job
specifics and characteristics such as strength requirement, body position at work, etc.
This corresponds to the “pattern recognition” process of decision-making. Disease-
11
oriented patterns contain little knowledge about pathology or physiology but a wealth of
clinically relevant information about the disease, its consequences, and associated signs
and symptoms. Accordingly, in a higher level of decision-making process, the clinician
may use some algorithms and clinical pathways to narrow down the described clinical
findings and specific clinical tests to a certain clinical diagnosis such as “rotator cuff
tear” and its associated prognosis. Utilizing a more advanced process of Hypothetico-
deductive model and based on the perceived cues from patient and environment, a
clinician may foresee potential challenges in returning this injured worker to his/her job
and finally makes specific RTW recommendation such as job modifications that are safe
and would meet the patient’s capacity and tolerance.
More recently Self Determination Theory (SDT), has been proposed as a
complementary framework that provides a unique “lens” to better understand the
underlying motivation that clinicians have toward particular counseling activities and
how conditions in their environment either facilitate or constrain these natural
tendencies[36]
. SDT proposes that 3 elements are essential to achieving optimal
motivation to action: 1) the need for autonomy or having a sense of choice in our actions;
2) the need for competence or the desire to act proficiently in our surroundings; and 3)
the need for relatedness or the importance of feeling supported and connected with
others[36]
. Clinicians may offer their patients a variety of advice and suggestions on
different treatment methods or on modifications of their behaviors towards their jobs as
an indicator of having autonomy in their clinical encounter. Besides, in order to be
proficient in their clinical evaluation and recommendation and apart from their primary
12
assessment, clinicians may use additional data provided by other health care providers
such as job-site assessments and functional ability evaluations as a result of their desire to
be competent in their clinical work setting. It is also possible that as a result of having
interaction with other colleagues, some clinicians adopt certain routine in their practice
that is reflective of their need for relatedness.
Clinicians get input from many sources of data and after a careful analysis use all
aspects of the decision making process to make their final recommendation. In a
systematic review, Stergiou-Kita et al. (2012)[37]
, proposed an inter-professional clinical
practice guideline for vocational evaluation following traumatic brain injury. In this
guideline, the evaluators were recommended to analyze their assessment results
thoroughly for adequacy and consistency and to relate an individual’s abilities to work
demands and environmental supports and to develop clear recommendation for future
vocational planning, providing specific evidence from their assessment results to support
their recommendations. Evaluators were also recommended to utilize evidence from
individual’s own experiences towards returning to work in order to ensure that their post-
injury work goals, self-perceptions and potential anticipated challenges are considered[37]
.
Apart from describing the many different variables that may seem relevant to the
clinician’s RTW recommendation, it is important to apply a systematic theory of
decision-making for RTW recommendation in upper limb injured workers. Such a theory
would relate the evidence from the literature, the predictors of interest according to the
work functioning framework and the clinician’s decision-making process with the
outcome variables (Figure-1).
13
The objective of this study is to focus on some of the potential predictors from
personal and environmental categories that determine clinicians’ decision on work
readiness of the workers with shoulder and elbow injury. According to our review of
literature and described theoretical framework these potential predictors are: age, gender,
level of education, occupation and job demands, clinical diagnosis, sleep disturbance,
pain score, disability index, general health index, and time elapsed since injury. We
describe these variables in the following chapter in more details.
14
15
2. Research Question and Hypotheses:
The specific research question to be addressed in this study is: “What are the
factors associated with clinician’s recommendations for RTW in patients with shoulder
and elbow work-related injury?” (See “Methods” for information on study population). In
this study by referring to clinician(s) we refer to the multi-disciplinary team consisting of
Orthopaedic Surgeons and Physiotherapists who assessed the injured workers in the
WSIB shoulder and elbow specialty clinic on the same clinic visit date.
We hypothesize that:
1- UEIWs with better mental and physical health (as measured by higher scores of
MCS and PCS components of SF-36 questionnaire) are more likely to receive a
recommendation for RTW by clinicians.
2- Workers with work-related injury to the soft tissue structures of shoulder and
elbow (as opposed to neurologic or non-specific conditions) are more likely to
receive a recommendation for RTW by clinicians.
3- Clinicians are less likely to recommend RTW to the UEIWs who have higher
demands and activity level at their job.
16
4- Clinicians are more likely to recommend return to work to workers with higher
levels of education.
5- UEIWs with a greater functional disability and or higher pain and disability
index are less likely to receive a RTW recommendation by clinicians.
6- UEIWs with a longer time interval since injury are less likely to receive a RTW
recommendation from clinicians.
17
METHODS
1. Study Design:
This study is secondary to an analysis of the baseline responses to a longitudinal
cohort conducted in 2006[38]
. The cohort sampled patients attending a WSIB Shoulder
and Elbow Specialty clinic (Holland Centre, Sunnybrook Health Sciences Centre) for
their first visit to have their shoulder/elbow condition assessed by a multi-disciplinary
team. At the time of that study eleven specialty clinics existed around the province and
injured workers needing assessment for recommendations to improve RTW success were
referred by the WSIB Specialty Clinic Office to the appropriate clinic. Workers might
have traveled a great distance to attend the clinic. The cohort consisted of four surveys
along with a detailed set of prognostic variables to support the research question of that
study. Only the baseline data was used for the current thesis.
Approval of the cohort study was received through the research ethics board
(REB) of Sunnybrook Health Sciences Centre, St Michael’s Hospital and the University
of Toronto. These approvals were updated to include the current authors’ name.
2. Inclusion and Exclusion Criteria:
In this study, all men and women between 18 and 65 years of age with shoulder
and elbow work-related injury from the existing one-year cohort of 303 patients who
18
were referred to the WSIB Shoulder and Elbow Specialty Clinic at the Holland Centre of
Sunnybrook Health Sciences Centre in Toronto, were at least 6 months since time of
injury and were not working at the time of clinic visit were included. Patients were not
included if they were unable to complete the described surveys in the English language
and communicate and give consent for the use of their data for research. Therefore,
considering the above criteria, a total of 141 patients were included.
3. Data Collection:
For the purpose of this thesis two sources of information were combined. First,
the baseline data gathered as part of the cohort study in a self-reported questionnaire, and
second information and clinical findings from the clinic records. All participants had
consented to the use of both of the sources of information.
3.1. Self-Reported Survey: Our focus was on factors that could be associated
with a recommendation of RTW in the clinical record. Our rationale for what this
information would be is described later in this chapter (Potential Predictors) and the
availability of these concepts were matched to the cohort baseline data. The self-reported
survey consisted of two different surveys. The first being the Injured Worker’s Survey
fielded to all injured workers at their initial assessment at the clinic. This survey which
blended descriptive information and standardized scales included a description of pain
location, descriptors of pain, staging of the nature of the pain using Von Korff’s chronic
pain grade[39, 40]
, disability (Disabilities of the Arm Shoulder and Hand) (Quick
19
DASH)[41-43]
and Short Form 36 item health status questionnaire, version 2 - Acute (SF-
36 Acute, V2)[44, 45]
. Other demographic and clinical information that were used from this
survey included patient’s age, gender, occupation at time of the injury, level of education,
injury date and clinic visit date. Patients were identified only by study ID and the data
were transcribed into a SAS compatible file and were saved in an encrypted USB key.
The second survey was a prognostic survey which fielded any remaining
prognostic factors found in a thorough review of the literature that were not part of the
Injured Worker’s Survey. The data from the second survey was not the focus of this
research and therefore was not used.
3.2. Clinical Chart Review: The remaining variables were extracted by
reviewing the clinical charts. Following approved procedures by thesis committee, a
template was created for the data extraction sheets consisting of two pages. The first page
contained patient identifiers including name, Hospital File Number (HFN), date of birth
and clinic date and study ID. This page was removed once data collection was completed
and served only to link the patient record with the study ID. It was stored in a separate,
locked file in the research office. This effectively de-identified the data used for the
purposes of this thesis. The second page contained check boxes and areas for narrative
description of the variables of interest including: clinical diagnosis and related ICD-9-
CM (The International Classification of Diseases, 9th Revision, Clinical Modification)[46]
20
code, prognosis, date of accident, time since injury, occupation at the time of accident, sleep
disturbance, clinician’s return to work recommendation (Form 1).
21
According to the inclusion criteria, a hard copy list of 141 patients’ name and their
related HFNs was created for the Holland Center’s medical record in order to pull the requested
clinical charts for review. Charts were held in the medical records department and two assessors
reviewed the data. The required data were then extracted from “WSIB Specialty Program
Summary Report”, “Shoulder and Elbow Specialty Clinic Multidisciplinary Healthcare
Assessment Report” and “WSIB Shoulder and Elbow Program Assessment Form”.
Two Assessors, FT and TI, were involved in this part of study and assessed 103
and 38 charts respectively. In order to highlight the potential inter-observer variability
and disagreement in data collection and to avoid future error in data collection, a pilot
chart review session was arranged and both assessors reviewed three charts and data were
collected and compared accordingly. In two of the variables some disagreement existed.
One of them was the time since injury, which was calculated either in months or
years. For most part it was an estimate of the actual time by the physiotherapist who had
completed the clinic assessment forms at the time of the clinic visit. In some other
instances the assessors had miscalculated the actual time. In order to eliminate this
variability, the time since injury was directly calculated by subtracting the clinic date
from the injury date that was provided from self-report survey (see Methods 5.1.6.).
The second variable was the diagnostic ICD-9-CM code. Different codes were
used at the time of the clinic for a certain clinical diagnosis and for some patients many
22
codes may had been recorded and therefore this had caused some variability in extracting
the appropriate code into our assessment sheet (Form 1) and it wasn’t clear as to what
code is the correct code. In order to address this variability, in patients with multiple
diagnoses only the one attributed to the worst symptoms or worst clinical prognosis was
considered and instead of using the ICD-9-CM codes the dictated clinical diagnoses by
clinicians were used (see Methods 5.1.4.). For example the rotator cuff tendinitis could
have been listed as rotator cuff impingement, rotator cuff tendinopathy, supraspinatus
tendonitis, etc. and recorded by different ICD-9-CM codes but in the end they were all
reflecting one form of the pathology. Furthermore one patient could have had two or
more different complaints or clinical diagnoses. Since all documented clinical diagnoses
were linked to a clinical prognosis in the clinic chart, it was decided by the thesis
committee to consider the one clinical diagnosis that was related to the least favorable
clinical prognosis. In situations where a similar prognosis was recorded, the clinical
diagnosis that contributed to the symptom with more severity or a higher level of
disability was considered as the primary clinical diagnosis. This decision was based on
the patients’ chief complaints or their subjective severity of pain or disability as
documented in the orthopaedic surgeon’s notes.
The completed forms (Form 1) were then kept in a locked filing cabinet in a
locked research office at the Holland Centre until all charts were reviewed. Data were
coded as necessary, and entered electronically into a computer database. It was then
transferred into a SAS compatible format and saved in an encrypted USB Key.
23
4. Outcome Variable - Clinician’s recommendation for RTW:
This outcome variable was obtained from the patient’s clinical chart in the
following categories and it only reflects the clinicians’ recommendation at the end of the
first clinical visit. Patients were grouped into one of the following categories:
4.1. No RTW: Patients were recommended not to return to their pre-injury
employment. This category also included those that were referred for additional
treatments (i.e. surgery); further diagnostic tests; or retraining and “Labor Market Re-
entry” (LMR) program in order to consider an alternate job, as they were not fit and safe
to return to their pre-injury employment.
4.2. Return to modified job (full or part-time): Patients were recommended
to return to their pre-injury employment with modified duties in terms of activity levels
or workload, and/or with modified hours on graduated basis. This also included situations
where patients were found by clinicians to be fit and safe for modified duties and
received one form of job modification recommendation by clinicians and at the same
time it was reflected in the clinical notes that they would not return to work due to
reasons such as patient’s job had been terminated by the employer or the job had been
closed down. In these cases the clinician’s recommendation for RTW was considered as
modified job although ultimately the patient was not returning to work.
24
4.3. Return to regular job: Patients were recommended to return to their pre-
injury employment without any change in their working hours, workload or activity
levels.
A preliminary descriptive analysis of the outcome variable was performed and it
was noted that a total of 25 (out of 141) patients either had missing or non-specific
clinician’s RTW recommendation. Therefore, in an attempt to decrease the number of
unclear outcome variable (clinician’s recommendation for RTW), a second chart review
was conducted. The study ID’s of these patients were matched with their names and
HFNs and their charts were re-reviewed. At this time the “Shoulder and Elbow Specialty
Clinic Return to Work Coordinator Assessment Report” that was provided and dictated
on the same clinic date by a RTW coordinator was reviewed by one of the assessors (FT).
In 14 patients, one of the three RTW recommendations (return to full or modified work or
no RTW) was retrieved from this report (reflecting the clinicians’ recommendation for
RTW (not the RTW coordinator recommendation) that was missing from their dictated
note in the WSIB Specialty Program Summary Report). The data from the remaining 11
patients with unknown clinician’s RTW recommendation was coded as missing.
Since only small number of patients (six) had received recommendation to return
to full work, the 3 groups of RTW recommendation were dichotomized into 2 groups of:
1- RTW (return to full or modified work - 91 patients) and 2- No RTW (no return to work
- 39 patients).
25
5. Potential Predictors:
A comprehensive review of literature was conducted using Ovid Medline and
Embase databases as well as PubMed. No article was found evaluating the factors that are
potentially associated with clinician’s recommendation for RTW in upper limb injured
workers. Therefore by utilizing the same databases, the articles assessing the factors
affecting RTW were searched using keywords including upper extremity injury, upper
limb injury, musculoskeletal injury, work related injury, vocational injury, shoulder
injury, elbow injury, employment, disability, orthopaedic injury, trauma, RTW, decision-
making, etc. All relevant articles were downloaded into Endnote to facilitate the review
of selected literature.
Based on the literature review, the different categories of factors reviewed in the
work functioning framework, the described theoretical framework, clinical judgment and
based on their availability in the data obtained from the study population, in this study,
the following variables were considered as potential predictors of clinician’s RTW
recommendation: age, gender, level of education, occupation and job demands, clinical
diagnosis, sleep disturbance, pain score (chronic pain grade), disability index (Quick
DASH score)[47]
, general health index (SF-36)[48]
and time elapsed since injury.
According to the described work functioning framework by Sandqvist et al[29]
,
most of our variables of interest are in the category of “personal factors”. Occupation and
job demands however, are considered as “environmental factors”. These mainly play a
26
role in the “work performance” and “individual capacity” dimensions of the proposed
work functioning framework and do not focus on the support of society for the
individual. It is important to note that variables are grouped according to their main area,
however, for variables such as education, occupation, and job demands, they also may
impact in other areas such as work participation level (Table 2).
Table 2. Category & Dimension of the Potential Predictors Affecting
Clinician’s Recommendation for RTW According to the Work
Functioning Framework
Study Variable Work Functioning Framework
Category Dimension
Age Personal Individual capacity/Work performance
Gender Personal Individual capacity/Work performance
Education Personal Work participation/Individual capacity
Occupation Environmental/Personal Work participation/Work performance
Job demand Environmental Work participation/Work performance
Diagnosis Personal Individual capacity
Time since injury Personal Individual capacity/Work performance
Pain index Personal Individual capacity
Disability index Personal Individual capacity/Work performance
General health Personal Individual capacity
Sleep disturbance Personal Individual capacity
The variables of interest are further discussed in two main categories of “Personal
factors” and “Environmental factors”.
27
5.1. Personal Factors:
5.1.1. Age: The effect of patient’s age in clinical decision-making is unknown.
Besides with respect to its effect on RTW, literature has been inconsistent showing
earlier return[49-51]
, later return[52-59]
or no effect[60-64]
with greater age. Younger
workers have a significantly higher return to work rate[18]
, which could be related to
their physical potential for a faster recovery. On the contrary, younger patients have
been also shown to have longer duration of disability and higher rate of injury
recurrence[14]
and this could make clinicians cautious when making recommendations
for their return to work. Therefore the effect of age seems to be still quite
controversial in RTW literature and unknown towards clinician’s decision. Age was
assessed as a continuous variable.
5.1.2. Gender: Not only is there a gender difference in the frequency of the
work-related injury[65, 66]
but also there are gender differences in response to
rehabilitation and RTW. It has been shown that men generally exhibit a higher mean
tolerance for pain and a higher pain threshold than women and female patients
display a greater sensitivity to pain[67]
. It is also believed that the differences in
attitudes about pain between the genders may have an effect on rehabilitation
response[68]
. In general, men are more likely to return to work after rehabilitation than
women[27, 63, 67, 68]
. The effect of gender in clinician’s decision for RTW however is
unknown and was assessed as a dichotomous variable (female versus male) in this
study.
28
5.1.3. Level of Education: Although some studies have shown that there is no
association between this variable and the possibility of return to work[55, 63, 69, 70]
, there
is some evidence that the level of education is a prognostic factor for the duration of
work disability[71]
. Selander et al. (2002) in a review article has reported subjects with
a higher level of education more often returned to work[72]
. Aronsson et al (2000)[73]
and Brox et al. (1996)[74]
have shown that those with higher education have lower
odds of presenteeism and lower level of disability and therefore have more motivation
for RTW, which may affect the decision of the clinicians assessing their condition.
Education was grouped into the following categories: 1= Grade 8 or less, 2= Some
high school, 3= Graduated from high school, 4= Attended college or technical school
but did not graduate, 5=Graduated from college or technical school, 6= Attended
university but did not graduate and 7= Graduated from university. According to the
literature[72, 75, 76]
, individuals with an educational level of vocational school/college
and higher have a higher chance of successful RTW. Therefore, education was
dichotomized into the following groups: 1-Education below college level and 2-
Education at or above college level.
5.1.4. Clinical Diagnosis: One of the important factors that clinicians include in
their assessment of injured workers is the individual underlying pathology and
clinical diagnosis. The proposed framework by Boocock et al, 2009[77]
, has classified
the different shoulder and elbow conditions. These conditions are also coded and
29
categorized as per the International Classification of Diseases, 9th Revision, Clinical
Modification (ICD-9-CM) diagnostic codes[46]
.
5.1.4.1. Shoulder Related Specific Conditions:
- Rotator cuff syndrome (including tendinitis and tear)
- Biceps tendon pathology (including tear of the long head and tendinitis)
- Arthritis
- Capsulitis (Frozen Shoulder)
- Other soft tissue injuries
5.1.4.2. Elbow Related Specific Conditions:
- Flexor-extensor tendinitis/ tenosynovitis- Epicondylitis (lateral or medial)
- Arthritis
- Olecranon bursitis
- Other soft tissue injuries
5.1.4.3. Neurological Conditions:
- Suprascapular neuropathy
- Radiating neck complaints
- Cubital tunnel syndrome
- Radial tunnel syndrome
30
5.1.4.4. Non-Specific Conditions: Such as myofascial pain syndrome,
chronic regional pain syndromes, referred pain to shoulder and elbow which are
characterized by non-specific pain, discomfort, fatigue, limited movement, loss of
muscle power, etc. These conditions have no anatomically localized pain profile
and are associated with regional pain.
In case of multiple diagnoses the one diagnosis correlating with worst prognosis
was considered. Prognosis for each one of clinical diagnoses was recorded by
clinicians in one of the following categories: 1= Significant recovery anticipated,
2=No significant changes anticipated and 3=Condition may deteriorate. In case of
equal prognosis the one, which had been attributed to the patient’s major clinical
complaint according to the clinician’s note, was considered as the primary
diagnosis.
With respect to the effect of clinical diagnosis on RTW in the work-related upper
extremity injuries, Feuerstein et al. (2003) showed that neuropathies in general are
more likely to result in no RTW in upper limb injured workers compared to
enthesopathies around the shoulder and elbow[60]
. Therefore the categories of
clinical diagnosis that are associated with slower recovery and return to work
were aggregated together and further contrasted with the specific shoulder and/or
elbow conditions (which are expected to RTW earlier), in the following
categories: 1) diagnoses attributed to Shoulder or Elbow soft tissue disorders and
31
2) others diagnoses (including the Neurological disorders and Non-specific
conditions of the Shoulder or Elbow).
5.1.5. Sleep Disturbances: These include difficulties initiating sleep,
intermittent and non-restorative sleep, and waking up too early. It has been shown
that sleep disturbance is associated with increased risk for subsequent disabling
mental disorders and various physical illnesses. Probability of not returning to work
after disability due to musculoskeletal disorders will increase in men and women who
experience severe sleep disturbances at baseline[78]
. Also Sonnenschein et al (2008)
has shown that trouble falling asleep and less refreshing sleep at baseline prevents
eventual full work resumption among workers[69]
. As a result, clinicians may suggest
some restrictions for RTW to assure safety for both patient and work place
environment.
5.1.6. Time Since Injury: Functional ability and pain severity are clearly related
to time elapsed since injury[21]
. Croft et al (1996) reports that a duration of shoulder
related symptoms greater than a month is significantly associated with poorer
functional outcome[79]
. Many studies have shown that a shorter duration between
injury and treatment or shorter duration of symptoms (especially less than 12 months)
is associated with lower disability and better function[72, 80-84]
. Therefore this is an
important factor for clinicians to consider when making RTW recommendation.
However, it is not always quite clear that at what point in time the injury has
occurred. Some of the injuries are the result of multiple or repetitive minor strains and
32
quite often the patients are not exactly certain of the exact date of onset. Ontario’s
WSIB encourages workers to report their injury to their employers as soon as possible
and file their claims to WSIB no later than 6 months from their date of injury or the
date they have learned about their condition[26]
. Since the date of submission of a
worker’s compensation claim was clear to the clinicians, in this study for the small
subgroup of patients that the injury date was not exactly known; the “Time Since
Claim” was used instead. The estimated time difference between the injury date and
date of compensation claim has not been studied in the literature. This variable is a
continuous variable that was calculated as the time interval in months between date of
injury/WSIB claim and date of first clinic visit at the WSIB Shoulder and Elbow
Specialty Clinic. For each patient the interval was first calculated in days and then
was dichotomized into “Time since injury > 12 months” (yes/no).
5.1.7. Chronic Pain Grade (Von Korff Pain Scale): Different degrees of pain
can impose different levels of disability and impairment on patients with work-related
shoulder and elbow injuries. Lower pain levels at baseline and improvement of pain is
associated with lower disability[83, 85-88]
, higher work role functioning[89]
and higher
chance of RTW[55, 90]
. We hypothesized that the magnitude and intensity of pain had
an important role on return to work restriction that is recommended by clinicians.
Many standardized pain questionnaires have been used to assess pain intensity and
characteristics[91, 92]
. In this study, in most patients some time had elapsed from the
time of injury when they were assessed by clinicians. Therefore it was appropriate to
use Von Korff pain scale which grades the severity of “chronic pain” based on its
33
characteristics and its impact on a person's activities[40]
. Its validity and reliability has
been shown in different populations such as Americans, Germans, British and
Brazilians with a variety of chronic pain problems including, chronic low back,
musculoskeletal, or temporomandibular joint pain and headache [39, 40, 93, 94]
. This
scale includes 7 questions, 6 of which get responses on a scale of 0 to 10 and one
question asks about the number of disability days. The advantage of this
questionnaire is that it includes all the pain symptoms within 6 months and
differentiates persons with intense pain who are not disabled from persons with
comparable pain who are significantly disabled[40]
.
The “Pain Intensity” was calculated by multiplying the mean of responses to question
number 1 to 3 (sum of responses divided by 3) by 10. The “Disability Score” was
calculated by multiplying the mean of responses to question number 4 to 6 by 10. The
“Point for Disability Score” was calculated as: 0 (0 – 29), 1 (30 – 49), 2 (50 – 69) and
3 (>= 70). The “Point for Disability Days” according to responses to question number
7 was calculated as: 0 (0 - 6 days), 1 (7 - 14 days), 2 (15 - 30 days) and 3 (>= 31
days). The “Disability points” was calculated by adding “Points for Disability Days”
to “Points for Disability Score”.
Von Korff suggests the use of the disability points and pain intensity in combination
to create a score reflecting chronic pain grade (CPG). Specifically they instruct the
assignment of patients to the categories as follows: 0- pain free (no pain problems for
the prior 6 Months), 1- low disability low intensity (characteristic pain intensity < 50,
34
disability points < 3), 2- low disability high intensity (characteristic pain intensity >=
50, disability points < 3), 3- high disability moderately limiting (disability points 3 or
4) and 4- high disability severely limiting (disability points 5 or 6)[40]
. In this thesis
the grades of 0 to 4 was used as a descriptor of the impact of long term pain and
disability.
5.1.8. Disability Index (Quick DASH): Patients’ reported functional
assessment following an occupational injury has been shown to be indicative of their
RTW[60, 62, 95]
. Several functional assessment tools have been developed to assess the
level of disability in UEIWs[92]
. The Disability of Arm, Shoulder and Hand (DASH)
measure[43, 47]
is a thirty-item questionnaire that quantifies physical function and
symptoms in persons with any or multiple musculoskeletal disorders of the upper
limb. Direct comparisons with other, more joint-specific or disease-specific measures
have shown the DASH to have comparable reliability and validity[42, 47]
. A major
advantage of the DASH is that it can be used for any upper-extremity evaluation and
therefore offers more versatility for clinical and research applications. The shorter
version of the DASH is the Quick DASH that contains eleven items and is similar
with regard to scores and properties to the full DASH. It demonstrates reliability,
validity, and responsiveness when is used for patients with either a proximal or a
distal disorder of the upper extremity[41]
. Similar to the DASH, each item has five
response options (1= no difficulty; 2= mild difficulty; 3= moderate difficulty; 4=
severe difficulty; 5= unable).
35
From the item scores, a summative score was calculated: Quick DASH
disability/symptom score = [(sum of n responses) – 1]/n x 25 (where ‘n’ equals the
number of completed responses)[41]
. The final score ranges between 0 (no disability)
and 100 (the greatest possible disability). In the calculation only one missing item
could be tolerated, and if two or more items were missing, the score could not be
calculated[42]
.
5.1.9. General Health Status (SF-36): General well being of injured workers
plays an important role in their ultimate outcome. Waylett- Rendall et al (2004) did
not find any significant correlation between SF-36 and RTW outcome in cases with
cumulative trauma disorder[96]
, however, many studies have shown that those workers
with better overall health status have a higher chance for recovery and RTW[55, 60, 63,
88]. The Short Form-36 (SF-36) is a multi-purpose, short-form health survey with 36
questions[48]
. All questions are scored on a scale from 0 to 100, with 100 representing
the highest level of functioning possible[45]
. It yields an 8-scale profile of functional
health and well-being scores (Physical Functioning (PF), Role-Physical (RP), Bodily
Pain (BP), General Health (GH), Vitality (VT), Social Functioning (SF), Role-
Emotional (RE), Mental Health (MH)), as well as psychometrically based physical
and mental health summary scores and a preference-based health utility index. It is
considered to be a reliable and valid instrument[97]
. It is a generic measure, as opposed
to one that targets a specific age, disease, or treatment group. Accordingly, the SF-36
has proven useful in surveys of general and specific populations, comparing the
36
relative burden of diseases, and in differentiating the health benefits produced by a
wide range of different treatments[45]
.
The SF-36 subscales were also combined according to the instructions of the
developers to form two scores: 1) Physical Component Summary score (PCS –
standardized against published general population norms based on United States
sample: mean=50, Standard deviation= 10 and norm range= 20-58), and Mental
Component Summary score (MCS – standardized against published general
population norms based on United States sample: mean=50, Standard deviation= 10
and norm range= 17-62). Each component summary score includes information from
all eight subscales with the PCS more heavily weighted towards PF, RP, BP, GH
domains and the MCS weighting more towards VT, SF, RE, MH[44]
.
Mental illness is the number one cause of disability in Canada, accounting for nearly
30% of disability claims and 70% of the total costs[98]
. Mental health as a predictor of
RTW has been studied in number of studies. In a study of 480 WSIB patients in
Ontario, Canada, with work related upper extremity injury[27]
, Pichora et al. (2010)
showed that patients with higher scores of Worker Instability Scale (WIS – a measure
of at-work disability)[99]
, have significantly (p<0.005) higher scores on mental domain
of Work Limitation Questionnaire (WLQ – 25, a measure of at-work disability)[100]
indicating greater limitation in this area. In addition, they found significantly lower
scores on the mental health component (MCS) of the SF-36 questionnaire for the
patients with high risk WIS (p<0.005), reflecting worse mental functioning. This
37
would suggest a role for mental health in predicting at-work disability for injured
worker and therefore it is important to include a measure of mental health for injured
workers in this study to see if the role extends to RTW recommendations made by
clinicians.
5.2. Environmental Factors:
5.2.1. Occupation: Several studies have shown that the job category and its
demand have an essential role on determining the RTW. It has been shown that
having a job with a higher physical demand, workload and activity level predicts a
slower and a less successful RTW[57, 101]
. Selander et al. (2002) in a review article has
shown that patients with a higher income are more likely to return to work[72]
. Also
the effect of different vocational sectors on RTW has been studied[72]
. As it was
described earlier, occupation and job demands are among the environmental cues that
clinicians may use in the decision-making process. Standard occupational
classification systems have been developed in many different countries. In this study
occupations were classified according to the Canadian National Occupational
Classification (2011)[102]
to: 1- Agriculture, forestry, mining, 2- Construction, 3-
Public administration, defense, 4- Transportation, communications, 5- Trade, finance,
insurance, 6- Manufacturing and 7- Community, business, service. In this study, only
the descriptive information on these categories is shown.
38
5.2.2. Job Demands & Body Positions: Jobs have been categorized in the
literature according to their physical and psychological demands. For example,
Karasek et al (1990) has described a "job strain" model, which states that the greatest
risk to physical and mental health from stress occurs to workers facing high
psychological workload demands or pressures combined with low control or decision
latitude in meeting those demands[103]
. For the purpose of our study we aggregated the
workers’ occupations into groups describing the physical demands of the occupation
and the primary type of posture or body movement involved at work. These
categories were based on the Human Resources & Skills Development Canada
(National Occupational Classification Career Handbook. Ottawa, ON: Government
of Canada; 2011)[104]
. In this Career Handbook, occupational characteristics are
assigned to each occupational code by trained occupational analysts using a modified
Delphi procedure[104]
.
Job demands relate to the strength requirements (the use of strength in the handling of
loads such as pulling, pushing, lifting and/or moving objects during the work
performed), of each occupation and is classified into the following four groups
according to HRSDC Career Handbook[104]
: 1 = Limited: Work activities involve
handling loads up to 5 kg. 2 = Light: Work activities involve handling loads of more
than 5 kg but less than 10 kg. 3 = Medium: Work activities involve handling loads
between 10 kg and 20 kg. 4 = Heavy: Work activities involve handling loads more
than 20 kg.
39
NOC codes are also classified according to the “Body Position”, which refers to the
primary posture of body position engaged in by the worker (according to HRSDC
Career Handbook and Statistics Canada 2001[104]
). Body position is categorized into
the following four groups: 1 = Primarily “sitting”, 2 = work involves “standing or
walking”, 3 = Working involving combinations of “sitting, standing and walking”,
and 4 = Work activities that involve postures such as bending, kneeling or crouching.
Many studies have emphasized on the negative role of a heavy job demands or a job
with more complex activity on a successful RTW. It has been shown that having a job
with higher demands and activity level predicts a slower and a less successful
RTW[57, 101]
. Therefore, based on this and given the study sample size considerations,
job strength requirements and job body position were further dichotomized into 1-
occupations that require the handling loads of 20kg or more (yes/no) and 2 - jobs that
required sitting only, versus those that did not.
5.3. Other Variables:
Some other variables have been studied in the RTW literature, however they were not
included in this study because they were either not collected (e.g. those related to
work stability or ergonomics), were not measured well (e.g. grip strength) or are
conflated with the outcome variable and were unlikely to be the focus of clinicians
when making their recommendations for RTW (e.g. diagnostic tests or treatment
40
recommendations that could potentially by their indication delay RTW in some of
patients). Examples of these variables are listed and discussed here:
5.3.1. Marital Status: It has been shown in the literature that single working
mothers may be at a greater risk for developing chronic medical problems, possibly
due to the burden of household and child-rearing activities[105]
. On the other hand
divorced/widowed male patients and the status of their parenthood has shown to have
no effect on their work disability[19]
. Most of the literature has failed to show any
association between this variable and return to work[54, 55, 63]
. In general it is reported
that married people are more likely to RTW than unmarried people[72]
. However, the
thesis committee agreed that clinicians generally make their recommendations
irrespective to the marital status of their patients and therefore this variable was not
studied in this research. It was only used to show the demographic description of
patients in the following categories: 1- Married/ living with partner; 2-
Divorced/separated; 3- Widowed and 4- Single.
5.3.2. Arm Dominance in relation to the injured site: It is hypothesized that
patients with non-dominant shoulder or elbow work-related injury may express lesser
disability. There are only a few studies in the literature about the association of arm
dominance and outcome and chance of RTW in patients with upper extremity
injuries. Some studies have indicated no difference in the rate of RTW after injuries
involving dominant versus non-dominant side[106]
. Seradge et al (1998) did not find
any association between arm dominance and the outcome of surgery in patients
41
undergoing cubital tunnel release[107]
. In another study by Shiri (2007) it was shown
that rotator cuff and bicipital tendinitis and medial epicondylitis were more prevalent
in the dominant arm only in women, whereas lateral epicondylitis was more prevalent
in the dominant elbow in both genders and the higher prevalence of rotator cuff and
bicipital tendinitis in the dominant side persisted beyond working age[108]
. In the same
study the prevalence of carpal tunnel syndrome did not differ by hand dominance[108]
.
On the other hand, involvement of the non-dominant arm or extremity has been
reported to be associated with fewer symptoms[81]
and better function[109, 110]
.
Although arm dominance may be related to the extent of the expressed symptoms, not
every occupation is dependent on the dominant arm and it was decided by the thesis
committee that it is unlikely for clinicians to consider this as a factor when making
recommendations for RTW for a patient who is already disabled and is receiving
WSIB benefits.
5.3.3. Surgery or Additional Treatment Recommendation: Suggesting further
treatment may decrease the overall disability and encourage a quicker recovery and
RTW[55]
. However, a recommendation for surgical treatment has been shown to be a
significant predictor of poor outcome in patients with work-related upper extremity
disorder[111]
. This may be due to the fact that the probability of return to work
decreases as the length of time off work increases and there are a lot of factors before
and after the surgery that could potentially increase this time off work[54]
. Proposing
an appropriate treatment may in fact improve the long-term likelihood of RTW and
since RTW recommendation immediately following the first assessment is the focus
42
of this study therefore this variable is not studied in this research. Furthermore, a
patient may receive a No-RTW recommendation because a surgery or other form of
treatments or paraclinical tests have been proposed, therefore these variables are
conflated with the outcome variable rather than being a true predictor of that.
5.3.4. Grip Strength: Grip strength has long been thought of as a possible
indicator of overall body strength. Smith et al (2006) found a direct correlation in grip
strength and overall body strength in very old and oldest females[112]
. Fry et al[113]
also found a correlation between grip strength and performance in American men
junior weightlifting. Wind et al (2010) showed that there is a strong correlation
between grip strength and total muscle strength[114]
. Therefore, grip strength could be
used as a general indicator for overall muscle strength. Health of the rotator cuff has
also been shown to be correlated to the grip strength. Yasou et al (2005) found that
grip strength had a significant correlation with the muscle strength of 45 degrees
shoulder abduction and external rotation in the affected (injured) side[115]
. A similar
study performed by Budoff (2004), revealed an increased prevalence of rotator cuff
weakness on the ipsilateral side of a hand injury or disorder[116]
. Bohannon R. W. has
shown that Dynamometer measurements of handgrip strength predict multiple
outcomes and handgrip dynamometry provides a valid indication of upper extremity
strength impairment.[117, 118]
Since grip strength was not routinely tested and, when
tested, documented by both physicians and physiotherapists in a variable manner it
was not studied in this research.
43
5.3.5. Work Disability Measures: When assessing the impact of occupational
injuries, clinical researchers are now recognizing the importance of considering
disabilities experienced by the workers while ‘‘at-work’’. Research in the broader
return to work literature indicates that workplace factors such as employer’s support,
early contact and intervention, work modifications and established disability
management programs can all improve return to work rates following work-related
injury. Many at-work disability measures have been used in workers with shoulder or
elbow disorders such as WIS (Work Instability Scale)[99]
, WLQ-25 (Work Limitation
Questionnaire - 25)[100]
, work module of DASH Questionnaire, etc.
Originally developed for Rheumatoid Arthritis (RA), the RA-WIS is a 23-item
questionnaire designed to assess the extent of mismatch between a worker’s
functional capabilities in relation to the demands at work, to provide an indication of
the extent of ‘‘work instability’’ (WI) experienced by the worker. Scale items
consider potential mismatches in terms of work ability, productivity and symptom
control. Unlike many other measures, the RA-WIS consists of binary response
options (yes/no), and is scored by summing the positive responses[99]
. The RA-WIS
demonstrates good psychometric performance suggesting a delicate balance between
job demands and work ability experienced by patients. It has high internal
consistency and reasonable construct validity. Besides, this measure is particularly
preferred by patients, likely in part due to the dichotomous nature of the scale items,
good length and the fact that it is more sensible and user-friendly to the patients
compared to other measures.
44
Although as an important part of the assessment, clinicians enquire about patient’s
occupation and ergonomics at work, and measurement tools such as WIS provide
great information about at-work disability; however in this study, the result of above
assessment tools were not generally available to clinicians and used by them at the
time of their assessment and therefore not evaluated in this study.
6. Data Management:
A decision was made by the committee to focus on only those workers in the
sample with complete outcome ascertainment before beginning any analysis. The main
outcome for this study was the RTW recommendation by clinicians. Eleven of the 141
patients (7.8%) were missing information on the primary outcome variable (which was
due to incomplete or insufficient medical documentation or because the clinicians were
awaiting further paraclinical tests, etc.) and therefore, their data was not included in the
analysis. The comparability of the sample with missing outcome values versus the 130
patients with valid outcome values is summarized in Table 3. Although in some of the
variables there seems to be some differences in the frequency (e.g. Gender, Marital
Status) or mean (e.g. Time since injury) between the two groups; however, they were not
statistically significant. In general the results in the table suggest that the removal of the
11 respondents with missing information (less than 10% of total study population) has
not biased the sample.
45
Table 3. Comparison of variables between the samples with available
(n=130) and non-available (n=11) clinician’s RTW recommendation for
patients with work-related shoulder and elbow injury
Categorical Variables (n/m) Outcome available
p-value Yes - N (%) No- N (%) Gender - Female (129/11) 67(52) 4(36) 0.36
Marital Status (128/11) 1- Married/Partner 2- Divorced/Separated 3- Widowed 4- Single
0.44
88(69) 9(82) 15(12) 2(18) 3(2) 0
22 (17) 0
Education (125/11)
1- Below college
2- College and above
0.52
73(58) 8(73) 52(42) 3(27)
Diagnosis (130/11)
1- Shoulder
2- Elbow
3- Neurologic
4- Non-specific
0.16
85(65) 5(46) 16(12) 3(27) 9(7) 2(18)
20(16) 1(9)
Diagnosis-Shoulder or Elbow (130/11) 101(78) 8(73) 0.71
Job Strength (130/11)
1- Limited (<5kg)
2- Light (5-10kg)
3- Medium (10-20kg)
4- Heavy (>20kg)
0.75
24(18) 1(9) 34(26) 2(18) 49(38) 6(55) 23(18) 2(18)
Heavy Job Demand (>20 kg) (130/11) 23(18) 2(18) 1.00
Job Body Position (130/11)
1- Sitting
2- Standing and/or Walking
3- Sitting, Standing and/or Walking
4- Other positions (bending, kneeling...)
0.93
24(18) 1(9) 27(21) 2(18) 14(11) 1(9) 65(50) 7(64)
Only Sitting Job (130/11) 24(18) 1(9) 0.69
Time Since Injury > 12 m (130/11) 59(45) 5(45) 1.00
Sleep Disturbance-Yes (126/11) 116(92) 10(91) 1.00
Continuous Variables (n/m) Outcome available
p-value Yes - Mean (SD) No - Mean (SD) Age - years (130/11) 43.5(9.8) 39.8(6.9) 0.20
Quick DASH Score (122/11) 64.4(18.8) 69.6(16.6) 0.55
PCS - SF36 (118/11) 35.9(6.4) 31.8(8.8) 0.23
MCS - SF36 (118/11) 35.9(11.8) 35.5(13.9) 0.71
Time Since Injury - months (130/11) 20.4(34.6) 44.18(62.19) 0.73
N=number of available or missing outcome variables observed; PCS= Physical Component Summary score (of SF-36 Questionnaire);
n= total number of predictor variable with available outcome variable; MCS= Mental Component Summary score (of SF-36 Questionnaire); m= total number of predictor variable with missing outcome variable; DASH= Disability of Arm, Shoulder and Hand;
SD= standard Deviation
46
7. Analysis: SAS statistical package (version 9.2 for Windows) was used for the
analysis.
7.1. Descriptive Analysis:
The demographic and descriptive results were presented and compared by median,
mean ± standard deviation for continuous variables. We also examined the distribution
(frequency) of individual variables across groups for categorical variables and across
levels of the outcome variable (Tables 4 and 5). Baseline characteristics of study
population were examined across each level of the dependent variable (Recommendation
for RTW vs. No RTW).
7.2. Univariate (Unadjusted) Analysis:
This step is the unadjusted evaluation of association of each predictor with the
outcome variable. For each independent variable we conducted univariate logistic
regression models to inform variable selection for our multivariate analysis. Variables
analyzed in this step included: Age, Gender, Time since injury, Time since injury > 12
months, Job strength, Job body position, Heavy job demand, Sitting-only job, Education
level (college and above vs. below college), Clinical Diagnosis, Diagnosis – Shoulder or
Elbow, Quick DASH score, Sleep disturbance and SF-36 major component summary
scores (PCS and MCS). Chronic pain grade (CPG) was not entered in this part of analysis
due to an insufficient number of observations in grades 1 and 2 for those patients who
47
received the recommendation of “No-RTW” (Table 5).
7.2.1. Variable selection: Only variables that had a significant or nearly
significant (p < 0.25) association with the outcome variable were chosen for the
next step (multivariate logistic regression analysis) to improve the validity of the
multivariate model[119]
.
7.2.2. Missing Cases in logistic regression: In many statistical packages
including SAS, the default function of logistic regression for cases with missing
variable is the “Listwise deletion” or “complete case analysis”, that is elimination
of a case when any of its variables has a missing data point, regardless of whether
that particular data point is being used in the analysis[120, 121]
. It was noted that in
this study, patients with a known outcome variable did not have necessarily
available data for all of the variables that were selected for the logistic regression
model. This would mean that the logistic regression analysis, for model
comparison, would only account for those cases that had available data on all the
predictive variables included in any model. It was revealed that in this study, after
the complete case analysis, only 111 patients would remain for final analysis and
model comparison and 19 patients were dropped out by the logistic regression
function.
48
7.3. Tests of Multicollinearity:
Multicollinearity of all candidate predictors (p-value < 0.25) was tested with VIF
(Variation Inflation Factor) and Tolerance (Table 8). VIF is a measure of how much the
variance of a regression co-efficient is inflated by the fact that other independent
variables contain the same information as the variable in question. Large values of this
diagnostic indicate sign of serious multicollinearity[122]
. Tolerance is the proportion of a
variable's variance that is not accounted for by the other independent variables in the
equation. A small tolerance value indicates that the variable under consideration is almost
a perfect linear combination of the independent variables already in the equation and that
it should not be added to the regression equation[122]
. If tolerance level is >0.4 or
Variance Inflation Factor is <2.5, it suggests that no significant multicollinearity among
the predictors is present and can therefore be tested for inclusion in the logistic model.
7.4. Multivariate Logistic Regression Analysis:
7.4.1. Sufficient events per variable: Calculating the maximum number (n)
of variables that can be permitted in the final model is quite important[123]
. The
dependent variable, Clinician’s recommendation for RTW, was categorized in
two groups of 33 (m) patients with “No RTW” and 78 patients with “RTW”.
Based on the rule[124]
of one variable allowed for every 10 observations in the
smallest category of the outcome variable (n=m/10), the maximum number of
variables that could be fitted in the final model was 3.
49
7.4.2. Exploratory analysis method: In this study the “Forward Manual
Selection” method was used to select the final variables to include into the model.
In this approach, independent variables were added to the model based on their
correlations with the unexplained component of the dependent variable[125]
. The
independent variable with the most significant association with outcome variable
in univariate (unadjusted) analysis was first added to the model. Then, other
variables with significant or near significant p-value association with the outcome
variable in univariate analysis were added one at a time and model statistics
especially the change in the chi-square (2) distribution and its related p-value
were compared in order to decide which variable would stay in the model and
then with the same approach a third variable was introduced to the model and the
model fit and statistics were compared (Table 9).
7.4.3. Model statistical parameters:
Test of Global Null Hypothesis (GNH): The GNH, in which all the parameter
estimates are equal to 0, was evaluated using the “Likelihood Ratio Test”[122, 126]
for all models. A significant p-value would determine that the model is able to
describe the data very well.
Goodness of Fit: All models were assessed for the Goodness of Fit using the
“Hosmer-Lemeshow Goodness-of-Fit test”[122, 126]
. A high and non-significant p-
50
value concludes that the null hypothesis (the data fits the model) cannot be
rejected.
Model Prediction: The predictability of the models was checked with the “C-
Statistics”[122, 126]
. It is a measure of rank correlation of ordinal variables and
measures the association of predicted probabilities and observed responses. In
other words it describes that how well the model can predict the outcome and it
reflects the area under the curve and ranges from 0.5 (no association) to 1 (perfect
association). Values above 0.7 are reasonable and indicate good model
predictability[127]
.
Tests of Comparison: These tests were used to choose the best model among the
models with similar tests of GNH, Goodness of fit and Predictability[126]
. “AIC”
(Akaike Information Criterion) was used for the comparison of models from
different samples but same number of variables (Non-Nested Models).
Ultimately, the model with the smallest AIC is considered the best. “-2 Log L”
(Negative two times the log likelihood) was used in hypothesis tests for “Nested
Models”. One model is considered nested in another model if all of its variables
are also included in the model to which it is being compared (along with one or
more additional variables). This statistic follows a chi-square (2) distribution and
therefore we can compare different nested models based on the critical values
associated with chi-square statistics for different degrees of freedom. In our
models the variables were either continuous or binary and their degree of freedom
51
was equal to 1. According to the chi-square probability table, for one degree of
freedom the critical chi-square statistic has a value of 3.841, therefore if inclusion
of the new variable changes the -2 log likelihood statistic by a value of 3.841 or
greater, it would make a more desirable model[119]
.
52
RESULTS
1. Descriptive Analysis: The results are presented in Tables 5 and 6.
Analysis of the continuous variables (Table 4) showed that population had a mean
age of 43.5 years (SD=9.8) closely distributed among groups of RTW (43.8 (SD=9.4))
and No-RTW (42.8 (SD=10.8)) recommendation. The average time since injury was 20.4
(SD=34.6) months (45% longer than 12 months) and in groups with RTW and No-RTW
recommendation it was 19.1 (SD=27.0) and 23.4 (SD=48.4) months respectively (42% of
RTW versus 54% of No-RTW recommendation group had a time longer than 12 months
since their injury).
General health assessment with SF-36 scores revealed an average PCS score of
35.9 (SD=6.4) with comparable means of 35.8 (SD=6.8) and 35.9 (SD=6.3) in groups
with No-RTW and RTW recommendation respectively. MCS score was on average 35.9
(SD=11.8), however the group with RTW recommendation had a higher mean of 38.4
(SD=11.1) compared with 30.1 (SD=11.4) in the group with No-RTW recommendation.
Our study population showed a quite high index of functional disability in all groups. The
Quick DASH score had a mean of 64.4 (SD=18.8) with a mean of 68.9 (SD=17.2) in No-
RTW and a mean of 62.5 (SD=19.1) in RTW recommendation group.
Analysis of the categorical variables is shown in Table 5. The outcome variable,
clinician’s recommendation for RTW, was available in 130 patients. Ninety one (70%)
53
patients were recommended to RTW (regular or modified job) as opposed to 39 (30%)
patients who had received a No-RTW recommendation. Fifty two percent of population
was female (equally distributed in both group); 69% of patients were married, 17% were
single and the rest were separated or widowed (with no significant difference between the
groups). Forty two percent had education at or above college level and this rate was 45%
and 33% in groups with RTW and No-RTW recommendation respectively. Chronic pain
grade analysis confirmed that 89% of the total population (95% in No-RTW and 85% in
RTW recommendation group) had high disability, which was moderately or severely
limiting. It was also shown that there was insufficient number of observations in patients
with low disability specially those in the No-RTW recommendation group.
With respect to the clinical diagnosis, 65% had shoulder specific conditions, 12%
had elbow specific conditions, 7% had neurological problems and 16% had non-specific
disorders. Seventy eight percent of clinical diagnoses (101 patients) were attributed to the
shoulder or elbow soft tissue disorders (67% in No-RTW and 82% in RTW
recommendation groups). Sleep disturbance was present in 92% of cases and it was
equally distributed in both groups of outcome variable. Job strength descriptive analysis
revealed that 23 patients (18%) had occupations with heavy (>20 kg of weight) demands
(26% in No-RTW versus 14% in RTW recommendation group). Analysis of job body
position showed that 24 patients (18%) had an occupation that involved only “sitting”
(13% in No-RTW versus 21% in RTW recommendation group).
54
Table 4. Descriptive statistics for continuous variables in sample of
injured workers attending shoulder and elbow specialty clinic (n=130)
Variables (n)(No-RTW/RTW)
Clinician’s
Recommendation
Clinician’s
Recommendation
No-RTW*
RTW*
N=39 N=91
Age - years (130)(39/91) 42.8 (10.8), 45.0
20-63
43.8 (9.4), 43.0
22.6-44.6
Time Since Injury- months (130)(39/91) 23.4(48.4), 12.5
6-310.2
19.1(27.0), 10.7
6-192.3
Quick DASH (122)(37/85) 68.9 (17.2), 75.0
29.5-100
62.5 (19.1), 65.9
9.1-95.5
SF-36
Score
PCS - Physical Component Score
(118)(35/83) 35.8 (6.8), 37.6
21.9-47.2
35.9 (6.3), 35.5
23.8-54.2
MCS - Mental Component Score
(118)(35/83) 30.1(11.4), 28.4
11.9-67.5
38.4 (11.1), 36.2
18.9-69.8
*For all variables: Mean (SD), Median
Observed range
55
Table 5. Descriptive statistics for categorical variables in sample of
injured workers attending shoulder and elbow specialty clinic (n=130)
Variables (n) Total
N (%)
Clinician’s
Rec.
No-RTW
N (%)
39(41)
Clinician’s
Rec.
RTW
N (%)
91(59)
Gender - Female (129) 67(52) 20(51) 47(52)
Marital Status (128)
Married/Partner
Divorced/Separated
Widowed
Single
88(69) 28(74) 60(67)
15(12) 4(10) 11(12)
3(2) 0 3 (3)
22 (17) 6(16) 16(18)
School (125)
1- Grade 8 or less
2- High school / some
3- High school / graduated
4- College/ not graduated
5- College/ graduated
6- University/ not graduated
7- University/ Graduated
5(4) 2(6) 3(3)
33(26) 12(33) 21(24)
35(28) 10(28) 25(28)
13(10) 3(8) 10(11)
27(22) 6(17) 21(24)
7(6) 2(6) 5(6)
5(4) 1(3) 4(4)
Education (125)
1- Below college
2- College and above
73(58) 24(67) 49(55)
52(42) 12(33) 40(45)
Diagnosis (130)
1- Shoulder
2- Elbow
3- Neurologic
4- Non-specific
85(65) 22(56) 63(69)
16(12) 4(10) 12(13)
9(7) 5(13) 4(5)
20(16) 8(21) 12(13)
Diagnosis-Shoulder or Elbow (130) 101(78) 26(67) 75(82)
NOC Code (130)
0- Management
1- Business, Finance & Administration
2- Natural & Applied Sciences
3- Health
4- Social Science, Edu., Government & Relig.
5- Art, Culture, Recreation & Sport
6- Sales & Service
7- Trades, Transport & Equipment operators
8- Primary Industry
9- Processing, Manufacturing & Utilities
3(2) 1(3) 2(2)
7(5) 1(3) 6(7)
1(1) 1(3) 0
5(4) 1(3) 4(4)
0 0 0
0 0 0
29(22) 12(30) 17(19)
50(38) 15(38) 35(38)
2(1) 0 2(2)
33(25) 8(20) 25(28)
Time Since Injury > 12 m (130) 59(45) 21(54) 38(42)
Sleep Disturbance-Yes (126) 116(92) 35(92) 81(92)
Heavy Job Demand (>20 kg) (130) 23(18) 10(26) 13(14)
56
Table 5. Continued - Descriptive statistics for categorical variables
in sample of injured workers attending shoulder and elbow specialty
clinic (n=130)
2. Univariate (Unadjusted) Analysis: The results are present in Table 6.
2.1. Variable Selection: Quick DASH score, MCS score, level of education at or
above college, diagnosis-shoulder or elbow, time since injury > 12 months,
sitting-only job (it’s p-value was very close to the proposed cut-off), heavy job
demand had a significant or nearly significant (p < 0.25) association with the
outcome variable (see Table 6) and were therefore considered for the
multivariable logistic regression analysis.
Variables (n) Total
N (%)
Clinician’s
Rec.
No-RTW
N (%)
39(41)
Clinician’s
Rec.
RTW
N (%)
91(59)
Job Strength (130)
1- Limited (<5kg)
2- Light (5-10kg)
3- Medium (10-20kg)
4- Heavy (>20kg)
24(18) 6(15) 18(20)
34(26) 10(26) 24(26)
49(38) 13(33) 36(40)
23(18) 10(26) 13(14)
Job Body Position (130)
1- Sitting
2- Standing and/or Walking
3- Sitting, Standing and/or Walking
4- Other body positions (bending, kneeling...)
24(18) 5(13) 19(21)
27(21) 9(23) 18(20)
14(11) 4(10) 10(11)
65(50) 21(54) 44(48)
Only Sitting Job (130) 24(18) 5(13) 19(21)
Chronic Pain Grade (CPG) (128)
1- Low disability – Low intensity
2- Low disability – High intensity
3- High disability – Moderately limiting
4- High disability – Severely limiting
7(5) 2(5) 5(6)
8(6) 0 8(9)
27(21) 8(21) 19(21)
86(68) 28(74) 58(64)
57
Table 6. Unadjusted analysis for clinician’s recommendation on
RTW versus each independent variable in shoulder and elbow injured
workers attending WSIB specialty clinic (n=130)
Variable
Maximum Likelihood Test Odds Ratio Estimates
N
Estimate p-value OR CI
SF-36
Score MCS (Mental Component Score) 0.0770 0.0009 1.080 1.032-1.130 118
PCS (Physical Component Score) 0.0025 0.9358 1.003 0.943-1.066 118
Quick DASH -0.0197 0.0827 0.980 0.959-1.003 122
DIAGNOSIS†
0.2260 130
1 vs 4 0.4688 0.1312 1.909 0.690-5.283
2 vs 4 0.5154 0.2851 2.000 0.473-8.462
3 vs 4 -0.8064 0.1348 0.533 0.109-2.616
DIAGNOSIS – Shoulder or Elbow (0 VS 1) -0.4260 0.0514 0.427 0.181-1.005 130
TIME SINCE INJURY (months) -0.0032 0.5303 0.997 0.987-1.007 130
TIME SINCE INJURY ≥ 12 m (0 VS 1) 0.2434 0.2062 1.627 0.765-3.462 130
JOB STRENGTHα
0.4752 130
1 vs 4 limited 0.2849 0.4642 2.308 0.669-7.962
2 vs 4 light 0.0618 0.8530 1.846 0.611-5.582
3 vs 4 medium 0.2049 0.5010 2.131 0.753-6.028
HEAVY JOB DEMAND (>20kg) (0 VS 1) 0.3636 0.1246 2.069 0.818-5.235 130
JOB BODY POSITIONβ
0.7390 130
1 vs 4 0.4140 0.3276 1.814 0.595-5.525
2 vs 4 -0.2279 0.5363 0.955 0.368-2.479
3 vs 4 -0.0047 0.9921 1.193 0.335-4.252
ONLY-SITTING JOB (0 VS 1) -0.2923 0.2825 0.557 0.192-1.619 130
GENDER - Female (0 VS 1) -0.0188 0.9218 0.963 0.454-2.043 129
AGE (years) 0.0108 0.5785 1.011 0.973-1.050 130
EDUCATION ≥ College (0 VS 1) -0.2451 0.2351 0.613 0.273-1.376 125
*Outcome assessed: “Return to Work” versus “No Return to
Work” recommendation; OR: Odds Ratio; CL: 95%
Confidence interval; Variables with bolded p-value are selected
for Logistic Regression Analysis. N: Number of observation
αJob Strength:
1- Limited (<5kg)
2- Light (5-10kg)
3- Medium (10-20kg)
4- Heavy (>20kg)
βJob Body Position:
1- Sitting
2- Standing and/or Walking
3- Sitting, Standing and/or Walking
4- Other body positions (bending, kneeling...)
†Diagnosis:
1- Shoulder
2- Elbow
3- Neurologic
4- Non-specific
58
2.2. Missing Cases in Logistic Regression Analysis: The intent of any
analysis is to make valid inferences regarding a population of interest. Missing
data threatens this goal if it is missing in a manner that makes the sample different
than the population from which it was drawn, that is, if the missing data creates a
biased sample. Therefore we performed an analysis comparing the basic statistics
of individual variables between the samples of UEIWs with available and missing
predictors in the logistic regression models (Table 7). We did not find any
significant difference between the two sample populations. Therefore we can
assume that respondents contributing to logistic regression analyses are likely
representative of the target population[128]
.
59
Table 7. Comparison of variables between the samples of UEIWs
with available and missing predictors* in the final logistic regression
model (n=111)
Categorical Variables (n/m) Predictors
available
N (%)
Predictors
missing
N (%)
p-value
RTW Recommendation - Yes (111/19) 78(70) 13(68) 0.87
Gender - Female (111/18) 56(50) 11(61) 0.40
Education (111/14)
1- Below college
2- College and above
0.39
63(57) 10(71)
48(43) 4(29)
Diagnosis (111/19)
1- Shoulder
2- Elbow
3- Neurologic
4- Non-specific
0.51
75(68) 10(53)
12(11) 4(21)
8(7) 1(5)
16(14) 4(21)
Diagnosis-Shoulder or Elbow (111/19) 87(78) 14(74) 0.66
Job Strength (111/19)
1- Limited (<5kg)
2- Light (5-10kg)
3- Medium (10-20kg)
4- Heavy (>20kg)
0.31
20(18) 4(21)
32(29) 2(11)
41(38) 8(42)
18(16) 5(26)
Heavy Job Demand (>20 kg) (111/19) 18(16) 5(26) 0.31
Job Body Position (111/19)
1- Sitting
2- Standing and/or Walking
3- Sitting, Standing and/or Walking
4- Other body positions (bending, kneeling...)
0.60
22(20) 2(11)
24(22) 3(16)
12(11) 2(11)
53(48) 12(63)
Sitting-only Job (111/19) 22(20) 2(11) 0.52
Time Since Injury > 12 m (111/19) 53(48) 6(32) 0.19
Continuous Variables (n/m) Predictors
available
Mean (SD)
Predictors
missing
Mean (SD)
p-value
Age - years (111/19) 43.1 (9.8) 45.9 (9.9) 0.26
MCS Score (111/7) 35.7 (11.6) 39.1 (15.8) 0.46
Quick DASH Score (111/11) 64.1 (18.3) 67.7 (23.3) 0.54
Time Since Injury - months (111/19) 21.5 (37.2) 14.2 (10.3) 0.39
*Predictors used in the logistic regression analysis (MCS score, Quick DASH score, Heavy job demand > 20 kg,
Education ≥ college, Sitting only job, Time since injury ≥ 12 months, Diagnosis-shoulder or elbow); n= total number of
patients with individual variable available among patients with all predictors available in the final logistic regression
model; m= total number of patients with individual variable available that were dropped due to “Listwise deletion” in the
final logistic regression model; SD= standard deviation.
60
3. Tests of Multicollinearity
Multicollinearity of all hypothetical predictors (p-value < 0.25) was tested
with VIF (Variation Inflation Factor) and Tolerance (Table 8). For all of the
variables, the Tolerance levels were >0.4 and the Variance Inflation Factors were
<2.5, suggesting that no significant multicollinearity among the predictors was
present and therefore were included in the logistic regression analysis.
Table 8. Test of Multicollinearity for independent variables with
significant or near significant p-value (<0.25) association with
clinician’s recommendation on RTW in shoulder & elbow injured
workers (n=111)
Variables Tolerance VIF
Quick DASH
0.80 1.24
MCS (Mental Component Score)
0.78 1.27
Education ≥ College
0.92 1.09
Diagnosis – Shoulder or Elbow
0.90 1.11
Heavy Job Demand (> 20 kg)
0.94 1.06
Sitting Only Job
0.91 1.10
Time Since Injury ≥ 12 months
0.98 1.02
No Multicollinearity observed as all Tolerance values are > 0.4 and all VIFs are < 2.5.
VIF: Variation Inflation Factor
61
4. Multivariate Logistic Regression Analysis:
4.1 Model Selection: From the variables that were shown to have significant or
near significant association with clinician’s recommendation for RTW in
univariate analysis, the variable “’MCS score” had the most significant p-value
(0.0009) and it was placed in “model 1”. Model statistics especially the 2 –
distribution (11.264) and its related p-value (0.0008) were noted, which also
revealed that the model had a plausible data predictability (> 70%) and model fit
(Table 8). Then other variables including “Quick DASH score”, “Heavy job
demand > 20 kg”, “Sitting-only job”, “Diagnosis – Shoulder or Elbow”, “Time
since injury ≥ 12 months” and “Education ≥ college” were added to model 1, one
at a time, to form models 2 to 7 (Table 8). Adding any of these variables to
“Model 1”, did not make any significant change in the 2 distribution
[119] (for 1df
(degree of freedom) and p<0.05, a ∆ -2 log L > 3.841 was plausible), indicating
that model fit was not improved (Table 9). For thoroughness of the analysis and
model comparison, a series of models with 2 additional variables in addition to
the variable “MCS score” were created (15 different combinations in total). None
of these combinations provided a superior model fit when compared with model 1
(for 2df and p < 0.05, a ∆-2log L > 5.991 was plausible).
62
The bolded p-values did not indicate any significant change in the 2 distribution of models 2 -7 when an independent variable was
added to the variable “MCS score” (p>0.05 for 1 df). Therefore, “Model 1” is the preferred model.
Arrow ( or ): reflects the direction of effect for each of the listed variables in relation to the clinician’s recommendation for RTW (full or
modified).
-2 log L: Negative two times the log likelihood (follows a chi-square (2) distribution and is used in hypothesis tests for nested models.);
2: Chi-square (is the -2 log likelihood (-2LL) of the intercept only model minus the -2LL of the model with all variables.);
∆-2 log L: The difference of the -2 log likelihood of the models being compared (x vs. y);
df: degree of freedom of the model (total number of binary and continuous variables or parameters);
AIC Statistic: Akaike Information Criterion (is used for the comparison of models from different samples or non-nested models.);
H&L Test: Hosmer-Lemeshow Goodness-of-Fit test;
C Statistic: A measure of model predictability.
Table 9. Multivariate Logistic Regression - Model comparison for
prediction of clinician’s recommendation for RTW in patients with work-
related shoulder & elbow injury (n=111)
Mod
el
Variables -2 log L
2
p-value 2
∆ -2 log L
p-value (df)
(x vs. y)
AIC
Statistic H&L
Test C Statistic
1
MCS score
123.835
11.264
0.0008
-
127.835
0.476
0.702
2
MCS score + Quick DASH score
123.737
11.363
0.0034
0.098
0.754 (1)
(2 vs. 1)
129.737
0.432
0.696
3
MCS score + Heavy job demand
> 20 kg
122.198
12.902
0.0016
1.637
0.201 (1)
(3 vs. 1)
128.198
0.410
0.705
4
MCS score +Diagnosis-shoulder
or elbow
122.600
12.500
0.0019
1.235
0.267 (1)
(4 vs. 1)
128.600
0.803
0.710
5
MCS score + Sitting-only job
123.792
11.308
0.0035
0.043
0.836 (1)
(5 vs. 1)
129.792
0.331
0.699
6
MCS score +Time since injury ≥
12 months
123.633
11.467
0.0032
0.202
0.653 (1)
(6 vs. 1)
129.633
0.406
0.717
7
MCS score +Education ≥ college
122.132
12.968
0.0015
1.703
0.192 (1)
(7 vs. 1)
128.132
0.402
0.727
63
4.2. Final Model: Table 10 summarizes the important statistical values of the final
model, which consists of only one predictor variable (MCS score). The calculated
p-value in test of GNH (Global Null Hypothesis) was highly significant
(p<0.005), determining that the final model was able to describe the data very
well. The Hosmer-Lemeshow Goodness-of-Fit test revealed a non-significant and
high p-value (0.476), indicating that the null hypothesis (the data fits this model)
could not be rejected. The C-Statistic showed a value of 0.702, indicating that the
final model in more than 70% of the time is able to predict the outcome. The
MCS score showed a significant association (p=0.0003) with RTW
recommendation by clinicians (OR: 1.07, 95% CI: 1.025 - 1.122).
64
Table 10. Final Logistic Regression Model – Factors Affecting
Clinician’s RTW Recommendation in UEIWs (n=111)
Predictor Degree of
Freedom
Direction of
Association*
Parameter
Estimate
p-value Adjusted OR
(95% CI)
Intercept
1
-1.501
0.0536
MCS – SF36
Score
1
0.070
0.0003
1.072
(1.025 - 1.122)
Model Statistics
Test Statistics
p-value
Omnibus Likelihood Ratio (2, p-value)
Test of Global Null Hypothesis
11.264
0.0008
Hosmer-Lemeshow Goodness-of-Fit
(2, p-value)
7.575
0.476
C-Statistics
0.702
*Arrow (): reflects the direction of effect for the independent variable (MCS Score) in relation to the clinician’s
recommendation for full or modified RTW (78/111).
65
DISCUSSION
The objective of this study was to focus on some of the potential predictors from
personal and environmental categories that determine clinicians’ decisions on work
readiness of workers with shoulder and elbow injury. We found that clinicians were
significantly more likely to recommend RTW in patients who demonstrated higher scores
in MCS component (better mental health) of the SF-36 questionnaire (OR: 1.07, 95% CI:
1.025 - 1.122). In multiple logistic regression analysis, the Odds Ratio (OR) of
independent continuous variables tend to be close to one and this does not suggest that
the coefficients are insignificant[129]
. The MCS score was an independent continuous
variable. Accordingly the value of its Odds Ratio is close to one (1.07). Since the
confidence limit did not contain the value of “1” (1.025 - 1.122) and the Wald statistics p-
value was highly significant (0.0003), we concluded that the described association was
highly significant. We did not find a similar relationship between physical health score
and recommendation to return to work. Taken together, these findings partially confirm
our first hypothesis regarding the role of physical and mental health on clinician’s RTW
recommendation.
We also found near significant associations between higher Quick DASH scores
(worse functional status) and heavy job demands and a reduced likelihood of clinicians
recommending return to work. In our population the means and medians of Quick DASH
score in both groups of workers with or without recommendation to RTW were quite
high indicating a population with high functional disability. This lack of variation in the
66
Quick DASH values may have led to finding a lower effect for the Quick DASH
measure, compared to if we had a population, which included some workers with lower
levels of disability. With respect to heavy job demands, it is possible that our study did
not have sufficient power to capture the difference of its effect on the two groups of
outcome variable. Higher level of education was associated with increased likelihood of
return to work in our univariate (unadjusted) logistic regression analysis, but it did not fit
into our final model. These findings weakly support our 3rd
, 4th
and 5th
hypotheses.
Other predictors such as: a) clinical diagnosis attributed to the soft tissue disorders of the
Shoulder or Elbow; b) shorter Time since injury (< 12 months); and c) job body position
that would involve only sitting; were also associated (with near significant p-value <
0.25) with a higher chance of receiving a recommendation to RTW by clinicians in the
univariate logistic regression analysis but were not associated with improved model fit in
our multivariate analyses, therefore not supporting our 2nd
and 6th
hypotheses. This could
be related to the effect of sample size and power of our study. With respect to time since
injury, the minimum time was 6 months, reflecting that all workers had chronic injury (>
6 months) and in some of them this time was even longer than a year (this was the cut off
that we considered in our final analysis).
Since this is one of the first studies to examine RTW recommendations among
UEIWs; we are unable to compare our results to previous studies in this population
group.[130]
In this study we found that a strong association exists between having a higher
score in MCS component of SF-36 questionnaire and receiving a recommendation to go
back to work. The MCS score has more weight towards the Vitality (VT), Social
67
Functioning (SF), Role-Emotional (RE) and Mental Health (MH) domains of the SF-
36[48]
. These subscales assess patients in order to determine if: 1-they have problem with
work or other regular activities due to any emotional problems such as feeling depressed
or anxious (RE); 2- their physical health or emotional problem interferes with their
normal social activities (SF); 3- they feel full of life and have a lot of energy or feel tired
and worn out (VT); and 4- they are calm, peaceful and happy or they are nervous,
depressed or they feel down (MH). These components in general reflect some aspects of
worker’s mental health along with their overall affect, mood and sociability. In this study
we are only able to comment on the associations that were seen and not their likely cause.
We can speculate, however, that someone who scored high on MCS score probably
presented to a clinician with a good mood and without any sign of anxiety and acted as a
person who was more likely to be compliant with treatment recommendations and ready
to follow the clinician’s directions for RTW. These associations require further testing
and investigation with future research. It would be reasonable to include a mental health
status screening test while assessing the shoulder & elbow injured workers in order to
identify those individual workers with an established mental health condition and, if
indicated, to refer them for appropriate assessment and treatment prior to proposing a
RTW recommendation.
While MCS scores had a significant association with clinician’s RTW
recommendation, the PCS scores that are heavily weighted towards physical domains of
SF-36 questionnaire (e.g. PF, RP, BP, GH), did not differ between the two groups of
workers who received recommendation for RTW and No-RTW (rejected our hypothesis
68
and accepted the null hypothesis). This could be due to the lack of variability of physical
component scores in our population (i.e. they were uniformly reflecting a lower physical
status), as our scores in the two groups were both well below normal values (means of
35.8 and 35.9 in two groups of outcome variable with normal values being 50) or may be
that it is not a good measurement tool for patients with upper limb injury. Therefore we
can conclude that mental health concerns are at least as important as worker’s physical
diagnosis and disability in clinicians’ evaluation of UEIWs and might be considered in
their decision-making before returning the individual worker to work.
It was also noted in our descriptive analysis (see Results - Table 4) that although
the observed range, mean and median of the SF-36 component summary scores (PCS and
MCS) were higher in the group of UEIWs who received a RTW recommendation as
opposed to those who received a No-RTW recommendation (as was hypothesized), the
mean of these scores were below the 2 SD value of the US population norms (Mean=50,
SD=10). This means that our study population generally had poorer physical and mental
health status in comparison to the US population norms. There is good evidence in the
literature that suggests being out of work-role for a long time (similar to our study
population) may negatively affect workers’ general well being and self-perceived health
status[131-133]
.
Chronic physical conditions are associated with higher risk of psychological
distress and poorer mental health[134]
. In this study we included UEIWs who were at least
6 months (average 20.4 months) from their date of injury of which 45% (54% in No-
69
RTW recommendation and 42% in RTW recommendation groups) were more than 12
months from their injury date. It is possible that at least some of the lower MCS scores in
this study population were secondary to an established mental health disorder and it
would be advantageous for some future research to determine if the chronic injuries act as
preceding factors for establishment of such disorders.
It is shown in the literature that self-reported upper extremity-specific health
status (as measured for example with the DASH score) correlates with depression and
pain anxiety[135]
. It is possible that workers in our study population, which had a high
mean Quick DASH score (64.4), were also suffering from some secondary psychological
problem due to their severe physical disability and pain and as a result had a generally
low mean MCS score. Indeed, our results might be suggesting that in persons with high
levels of physical disability, the decision-making around RTW is influenced by
psychosocial factors associated with coping with chronic and high levels of pain and loss
of physical functioning.
Schreuder et al. (2012) studied the inter-physician agreement on the readiness of
employees to return to work and concluded that return to work recommendations should
not be solely based on the knowledge and experience of clinicians and suggested the need
for an instrument to establish an employee’s readiness to return to work more
consistently[136]
. Standardized instruments such as SF-36 are important tools in clinicians
armamentarium and have a greater chance of having acceptable consistency and
reliability of work-related assessments[137]
. Due to our study limitations we were not able
70
to determine if the result of assessments by standardized tools were available to clinicians
(e.g. WLQ-25 or WIS) or used by them (e.g. SF-36) when making their recommendation
for RTW and this could be a topic for future researches.
Our study results are unique in that to our knowledge no previous research has
specifically investigated the potential predictors affecting clinicians in making
recommendations to patients with work-related shoulder and elbow injury for returning
or not returning to work. According to the described theoretical framework[29]
, we were
able to include variables that could be potentially associated with our outcome variable
from different categories such as those related to the person or environment and also from
different dimensions. Our described methodology was precise in collecting data; the
utilized cross-sectional study design was the most appropriate way of capturing a snap
shot of the potential factors affecting clinicians in their first clinical encounter with
UEIWs; and the multivariate logistic regression analysis was used as the most appropriate
analytical method for this type of study.
In this study, the data from the clinical charts was extracted by two individuals.
Although having one individual in charge of this task would ideally eliminate the inter-
observer variability in data collection, we addressed this issue by performing a pilot data
collection and comparing the collected data between the extractors and obtained
agreement on the method of data collection for the observed variations.
Our findings are limited by the variables that were collected through the cohort
71
questionnaire or retrospectively obtained through chart review. We were not able to
analyze some of the variables of interest, such as CPG (Chronic Pain Grade) by the
logistic regression model due to inadequate numbers of observations. We believe that a
larger sample size could have led to a greater statistical power and a better chance in
revealing other significant associations between the investigated predictors and
clinician’s recommendations. Also a larger sample size would have made a narrower
confidence interval for the calculated Odds Ratio, thus making the related findings more
precise. We also missed 19 cases in our final logistic regression model due to the
Listwise deletion or complete case analysis. Although the number of missing cases was
relatively small (about 14% of total cases) in comparison to the total sample size (n=130)
and Hertel (1976)[138]
recommended that Listwise deletion only be used if it leads to loss
of less than 15% of cases, this could have decreased the statistical power of the study and
since these missing data occurred at random, therefore it did not affect our findings’ final
estimate except potentially its precision[139, 140]
.
Our research question necessitated a cross-sectional study design in order to
capture the hypothesized associations at a single point in time. In this study we were not
able to follow-up the injured workers in order to determine the proportion of those
workers who had or did not have a successful RTW.
The recommendation to RTW needs to be viewed as both a patient and a work
place issue[141]
. In our primary review of literature we learned about some other variables
as potential predictors of recommendation for RTW such as WIS (Work Instability
Scale). These variable were either not available to the clinicians or were not utilized or
72
documented by all of them in this study. Similar to the studies performed on some
clinical conditions such as those with traumatic brain injury[141]
, it seems reasonable to
search for potential predictors of clinicians’ recommendation for RTW in upper limb
injured workers through qualitative research methodology.
Medical clearance for RTW is an inherently subjective process that is likely
subject to clinicians’ belief, bias, emotion and their susceptibility to the patient’s
motivation as well. Our study focused on the importance of utilizing standardized tools
such as SF-36 (specifically MCS score in our study) and objective clinical findings.
Nevertheless it is likely that the patients’ subjective complaints of pain and/or their
motivation are also influential in return to work decisions by clinicians. Watson et al.
(2009) performed an online survey among 125 surgeons about RTW after surgical
treatment of patients with forearm fractures and they found that although objective
factors such as job demands, grip strength, etc. may be the major determinants of
physician clearance to return to work, physicians are also influenced by patients’
motivation for RTW[142]
. Therefore clinicians may be led into decisions that are not
necessarily based on their observed clinical findings and measurements. Investigations on
such factors are examples for potential future research areas.
Practice guidelines are systematically developed statements, which aim to assist
practitioners and patients in making health care decisions about specific clinical
diagnoses and circumstances. They are designed to provide a link between the best
available evidence and clinical practice by making explicit recommendations to improve
73
health care services and outcomes[37]
. Practice guidelines have been designed and
proposed for many different clinical conditions and some have focused on return to work
recommendations for patients suffering from Diabetes or after Stroke or Traumatic Brain
injury[37]
. We hope that our study results, along with future findings in this field,
contribute to establishing guidelines for the evaluation of shoulder or elbow injured
workers with respect to their vocational readiness especially for clinicians with less
experience in making recommendation for RTW. Understanding how these decisions are
made and the factors leading clinicians to recommend RTW is a multifactorial process
that relates to cognitive and psychomotor skills of clinicians as well as the process of
clinical decision-making and we hope that this study has opened more doors for future
research in this area.
74
CONCLUSION
Our study demonstrated that our cohort of workers with chronic shoulder and
elbow injuries and an overall poor general health status and disability index were more
likely to receive a recommendation for RTW by clinicians if they had better mental
health status as assessed by the MCS score. This finding suggests that in our study
population the MCS score (an index of mental health status) had more weight than PCS
score (an index of physical health status) in predicting the clinicians’ recommendation for
RTW in injured workers with chronic shoulder and elbow disorders. Future studies using
other measurement tools for pain and disability, mental and physical health status as well
as at work disability measures will be helpful. Consideration should also be given to the
use of other methodologies such as qualitative research to further elucidate the predictors
of clinicians’ recommendations for return to work in workers with chronic shoulder and
elbow injuries.
75
FUTURE DIRECTIONS
The focus of this study was on factors that affect clinicians in making return to
work recommendation for patients with work-related shoulder and elbow injury through a
quantitative cross-sectional study design. To complement our findings, it would be
appropriate to also investigate the clinicians’ perspective and opinion directly through a
qualitative research aimed to explicate the personal client factors and workplace
environmental factors that would be considered most relevant to the evaluation of their
work readiness leading to a RTW recommendation. To accomplish this goal, the
orthopaedic surgeons that were involved in the assessment of injured workers in the
WSIB shoulder and elbow specialty clinic could be interviewed directly or be invited to
participate in a survey assessing which personal or environmental factors they perceive to
be relevant indicators of future success or risks of failure when evaluating a client’s work
readiness.
Certain information such as scores of Quick DASH and SF-36 questionnaires
were available to all clinicians in this study. We were not able to determine if the scores
were used by the clinicians at the time of their decision-making. Also the objective
clinical findings such as grip strength were measured and recorded by different
physiotherapists and were not documented for all of the patients. A future prospective
study design that would include all of these variables perhaps could determine whether-
or-not an association with RTW recommendations exists. Such a study would be helpful
in determining the relevance of this data in the assessment process.
76
In this study we used the PCS score of SF-36 as an index of physical health status.
Although we did not find any association between this variable and clinician’s RTW
recommendation, it is possible that the PCS score is not the best tool for physical health
status of the shoulder and elbow injured workers. We recommend the future studies
investigate the role of other physical health measures[143]
instead of PCS score in
determining clinicians recommendation for RTW.
In our study when we described our theoretical framework, we emphasized the
importance of environmental factors such as job demands, and levels of work functioning
such as work participation and work performance levels. As it was described earlier there
are different tools that can be utilized in these domains to determine levels of work
functioning. For instance, WIS and WLQ-25 are examples of tools for measuring at-work
disability indices. In this study, these data were not available to the clinicians and it
would be of interest to determine if availability of such information to the clinicians
would affect their decision-making for RTW at the time of evaluation. This could provide
a direction for a future longitudinal study.
RTW is a multifactorial process and involves clinicians, individual workers,
employers and the insurance industry, government and ultimately society as a whole. In
addition to investigating the factors attributed to clinicians’ decision making, it would be
crucial to know how these recommendations influence the final outcome of injured
workers. For example it is important to determine how many of those injured workers
77
who received a recommendation for RTW actually did RTW successfully considering all
of other factors related to their employer, work place, etc. This would require assessing
the injured workers prospectively and in regular follow-up visits.
Our research finding suggested that the upper limb injured workers with poorer
mental health status are more likely to receive a No-RTW recommendation. However, it
is unclear to us as to whether these workers were suffering from a psychiatric condition
such as depression that subsequently affected their mental health scores and their work-
readiness, or their poor mental health condition was secondary to their long-term physical
disability or being out of work-role for a long time. We suggest future studies to focus on
screening the mental health condition of the injured workers so clinicians can direct
patients, if appropriate, for assessment and treatment prior to making a decision on their
work-readiness. We also recommend comparative studies to investigate the clinicians’
decision-making factors for RTW in groups of patients with more heterogeneous
population that would also include workers with less chronic injuries (less than six
months duration) as well as those with lower disability indices.
From the clinical decision-making perspective, clinicians get input from many
sources of data by using their cognitive and psychomotor skills, and utilize the obtained
data to evaluate and diagnose the work-related injury and eventually to make RTW
recommendations through series of decision-making processes or strategies. An
individual clinician’s medical clearance for RTW is subject to their personal beliefs,
biases and emotions as well as their perception of their patient’s motivation for RTW. In
78
this study we did not explicate such skills, strategies and potential biases and these are
future research areas that could be investigated, possibly with qualitative research
methodologies.
Currently there is no evidence-based clinical practice guideline for vocational
evaluation following work-related shoulder and elbow injury explicating the essential
processes and relevant factors in evaluation of work-readiness for health care teams to
foster collaborative decision-making. We hope that findings of our study along with
similar future studies take the essential steps towards establishing practical guidelines
that will aid clinicians in determining whether or not an injured worker is able to work,
and in making recommendations regarding their work entry based on the scientific
evidences.
79
Appendix 1: Project Approval Letter - Research Ethics Board of
University of Toronto
80
Appendix 2: Project Approval Letter - Research Ethics Board of
Sunnybrook Health Sciences Centre
81
Appendix 2: Project Approval Letter - Research Ethics Board of
Sunnybrook Health Sciences Centre - Continued
82
Appendix 3: Permission Letter (email) from Original Author for
Reproducing the Work Functioning Framework Figure
83
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