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Health Conditions: Effect on Function, Health-Related Quality of Life, and Life Satisfaction After Traumatic Spinal Cord Injury. A Prospective Observational Registry Cohort Study
To analyze relations among injury, demographic, and environmental factors on function, health-related quality of life (HRQoL), and life satisfaction in individuals with traumatic spinal cord injury (SCI).
Design
Prospective observational registry cohort study.
Setting
Specialized acute and rehabilitation SCI centers.
Participants
Participants (N=340) from the Rick Hansen Spinal Cord Injury Registry (RHSCIR) who were prospectively recruited from 2004 to 2014 were included. The model cohort participants were 79.1% men, with a mean age of 41.6±17.3 years. Of the participants, 34.7% were motor/sensory complete (ASIA Impairment Scale [AIS] grade A).
Interventions
None.
Main Outcome Measures
Path analysis was used to determine relations among SCI severity (AIS grade and anatomic level [cervical/thoracolumbar]), age at injury, education, number of health conditions, functional independence (FIM motor score), HRQoL (Medical Outcomes Study 36-Item Short-Form Health Survey [Version 2] Physical Component Score [PCS] and Mental Component Score [MCS]), and life satisfaction (Life Satisfaction-11 [LiSat-11]). Model fit was assessed using recommended published indices.
Results
Goodness of fit of the model was supported by all indices, indicating the model results closely matched the RHSCIR data. Higher age, higher severity injuries, cervical injuries, and more health conditions negatively affected FIM motor score, whereas employment had a positive effect. Higher age, less education, more severe injuries (AIS grades A–C), and more health conditions negatively correlated with PCS (worse physical health). More health conditions were negatively correlated with a lower MCS (worse mental health), however were positively associated with reduced function. Being married and having higher function positively affected Lisat-11, but more health conditions had a negative effect.
Conclusions
Complex interactions and enduring effects of health conditions after SCI have a negative effect on function, HRQoL, and life satisfaction. Modeling relations among these types of concepts will inform clinicians how to positively effect outcomes after SCI (eg, development of screening tools and protocols for managing individuals with traumatic SCI who have multiple health conditions).
A traumatic spinal cord injury (SCI) is a devastating event with negative physiological, psychological, and social consequences for individuals, and an economic burden on society. In addition to the more immediate neurologic effects on motor, sensory, and autonomic functions, individuals are at higher risk of developing complications directly or indirectly influenced by the presence of impairment.
Common complications after (or secondary to) SCI include, but are not limited to, spasticity, urinary tract infections, pneumonia, depression, neuropathic pain, pressure ulcers, and autonomic dysreflexia, whereas others (eg, venous thromboembolism, pathologic fractures) are less common.
In addition to complications after injury, the effect of preexisting comorbidities must be considered, especially with increasing age at injury and associated increase in comorbidities; 92.9% of those >60 years of age have comorbid conditions compared with just 46.7% of individuals <60 years.
In this study, the term health conditions is used to describe both complications and preexisting comorbidities which may affect postinjury health status and other outcomes (eg, life satisfaction).
Health conditions can result in inability to work, decreased community participation, and increased health-related costs, all of which negatively affect quality of life.
used path analysis to examine the complex relations among aspects of SCI, health conditions, and personal factors (eg, age) with health-related quality of life (HRQoL) and life satisfaction, demonstrating that severity of SCI, number of health conditions, and personal factors (eg, older age) can all negatively affect HRQoL factors (eg, physical and mental health, life satisfaction). In addition to the association with participation, HRQoL, and life satisfaction, other previous studies have demonstrated that a count of health conditions is more predictive of both health status
The management of multiple health conditions (or multimorbidity) may be difficult because treatment of coexisting health conditions may conflict and exacerbate others; physicians have expressed that they are often overwhelmed when deciding on management of multiple health conditions.
Multimorbidity is also associated with disproportionate costs; one third of patients in the Veterans Affairs system had ≥3 health conditions, yet accounted for two thirds of total health care costs.
the International Classification of Functioning, Disability and Health (ICF) model developed by the World Health Organization was published, which recognizes the importance of both environmental (eg, living setting) and personal factors (eg, age) on body structures and functioning, activity, and participation.
Field M.J. Jette A.M. Martin L. Workshop on disability in America, a new look: summary and background papers. National Academies Press,
Washington, DC2006
The objective of this article is to explore the effect of health conditions on function, HRQoL, and life satisfaction in individuals with traumatic SCI and determine their associations with injury, personal, and environmental factors. Our hypothesis is that a higher number of health conditions will be associated with lower function, HRQoL, and life satisfaction.
Methods
Study cohort
Participants were obtained from the Rick Hansen Spinal Cord Injury Registry (RHSCIR), which includes 18 acute care and 13 rehabilitation hospitals across Canada. The RHSCIR is a prospective observational registry that collects data from adults with an acute traumatic SCI treated at an RHSCIR site to answer a priori research questions and facilitate best practice implementation; full details of the development of the registry and recruitment processes have been previously published.
All sites obtained local ethical approval before enrolling patients. A core dataset is collected for all participants, and more detailed datasets for consenting participants include regular community follow-up questionnaires.
The study cohort included participants with community follow-up data 9 to 18 months postdischarge to allow for community reintegration and participation. A neurologic examination was performed on admission to inpatient care according to the International Standards for Neurological Classification of Spinal Cord Injury,
neurologic severity by the ASIA Impairment Scale (AIS; where grade A is most severe and grade D is least severe), and level of injury (grouped as cervical [C1-T1] vs thoracolumbar [T2-L2]). Participants with level of injury L3 and below were excluded from the analysis given the mixed nature of their injury pattern (cord, conus, and/or cauda equina injuries).
Model data
A 5-layer conceptual model was developed using the modified ICF model
(see fig 1), available RHSCIR data, and the hypothesized relation among the study variables. Layer 1 includes injury, personal, and environmental factors. Layers 2 through 5 include outcome variables, including health conditions, function, HRQoL, and life satisfaction, respectively. All associations between all variables were assessed.
Layer 1: SCI, personal factors, and environmental factors
Spinal cord injury
Neurological severity (AIS grade) and level of injury were measured as already described at the time of admission to inpatient care.
Personal factors and environmental factors
Age at injury, sex, and body mass index (BMI) were collected at time of injury. The following variables as collected at the time of follow-up (9–18mo postdischarge) were also included: education level (<high school vs ≥high school), living setting (own home/rent vs other [eg, care home]), living situation (living alone/with family members/with nonfamily members), marital status (married/common law vs single/divorced/separated/widowed), and employment status (employed/other). Variables with 3 levels were dichotomized because of the requirements of the modeling software.
Outcomes
Layer 2: health conditions
Health conditions include preexisting comorbidities and postinjury complications. Although it was not possible to differentiate these because time of onset was not available, our intent was to assess effect of multiple health conditions; therefore, including both was appropriate. Twenty specific health conditions were coded as present versus absent at follow-up. In addition to the 20 items queried, participants could report other health conditions; these were grouped by 8 body systems (eg epilepsy as other-neurologic) as listed in table 1. Pain was assessed with the presence of the bodily pain question in the Medical Outcomes Study 36-Item Short-Form Health Survey (Version 2) (SF-36V2
) and dichotomized as none/very mild versus mild/moderate/severe/very severe. The total number of health conditions was calculated and used in analysis given its aforementioned associations with outcomes in the literature.
Table 1Summary of the presence of health conditions that were collected at the time of follow-up in the analysis cohort (N=340)
motor score obtained by interview at follow-up was used to assess function. It includes 13 questions, assessing self-care, sphincter control, mobility, and locomotion and is scored from 1 (complete dependence) to 7 (complete independence). The individual question scores were totaled (range, 13–91).
Layer 4: HRQoL
HRQoL assesses the effect of health conditions on aspects of physical and mental functioning, including limitations in usual role activities (eg, work, social).
The PCS includes questions such as limitations on activities (eg, bathing, dressing) and if the individual's physical health has caused difficulties in the amount of work they can do. The MCS includes questions such as limitations in interaction with family and how frequently an individual has felt depressed. Both PCS and MCS were developed using normative scoring, scaled to a mean of 50 and an SD of 10; details on scoring are described elsewhere.
The LiSat-11 includes 11 items scored on a 6-point scale from 1 (very dissatisfied) to 6 (very satisfied) over various life domains (eg, personal relations, physical health). A total score was calculated as per other published articles
Validity of the Life Satisfaction Questions, the Life Satisfaction Questionnaire, and the Satisfaction With Life Scale in persons with spinal cord injury.
First, a descriptive analysis was conducted. Next, a 5-layer path analysis was performed to examine the relation between layer 1 (SCI and personal and environmental factors) and layers 2 to 5, which included the outcome variables (health conditions, FIM, PCS, MCS, and LiSat-11). Age, BMI, PCS, MCS, FIM, and LiSat-11 were analyzed as continuous variables. Education, marital status, living setting, and living situation were dichotomized as described. The number of health conditions was a number between 0 and 29. Because imputation of outcome variables (levels 3–5) may introduce bias, the sample size of those with complete data was sufficient; therefore, the results of the model analysis apply to only those with complete outcome data.
Path analysis was conducted using Mplus version 7.11.a We started with a saturated model and used a backward selection process to remove nonsignificant variable associations. Model fit, indicating how close the model is to the study data, was assessed with 5 fit indices: a chi-square test (P>.05 was considered good fit), the root mean square error of approximation (<.05 was considered as close fit, and upper value of .080 was considered reasonable fit),
Distributions of the residuals were checked to determine if there were any deviations from normality or sign of homoscedasticity. Estimates were also standardized to allow for comparison of the magnitude of effect on outcomes.
Results
Sample description
There were 580 participants who met the study inclusion criteria (fig 2). Of these, 240 (41%) had incomplete outcome data (levels 3–5), meaning the results apply to the 340 with outcome data.
Fig 2Participant flowchart. Abbreviation: CFU, community follow-up.
The modeling cohort (N=340) had a mean age at injury of 41.6±17.3 years, 79.1% were men, 34.7% were motor/sensory complete (AIS grade A), 61.8% had cervical injuries, and the mean achieved time from discharge to community follow-up (based on participant availability) was 11.7±2.6 months. Eighteen participants in the analysis cohort completed their FIM on a different date than the collection of the other follow-up outcome measures because of the availability of a trained FIM assessor. In these 18 cases, the FIM motor score nearest to the other follow-up data was used (median of 1mo before other follow-up data). Table 2 provides complete details of the analysis cohort, reflecting the variables included in layer 1 of the model (SCI, personal factors, and environmental factors). To assess for potential bias caused by excluding those with missing data (240 exclusions vs 340 inclusions), we compared age, sex, severity (AIS grade), and level of injury (cervical/thoracolumbar). The only statistically significant difference was the analysis cohort was slightly younger than those excluded (mean age, 41.6 vs 48.9y, respectively; P<.0001).
Table 2Overview of the model analysis cohort (N=340)
Variable
Model Cohort
Age at injury (y)
41.6±17.3
Male
269 (79.1)
Neurologic severity of injury (AIS grade)
A
118 (34.7)
B
40 (11.8)
C
55 (16.2)
D
127 (37.4)
Neurologic level of injury
Cervical (C1-T1)
210 (61.8)
Thoracolumbar (T2-L2)
130 (38.2)
BMI
25.7±5.4
Time from discharge to follow-up (mo)
11.7±2.6
Education level
Less than high school
64 (18.8)
High school or above
276 (81.2)
Living setting
Own home or rent
317 (93.2)
Other (eg, care home)
23 (6.8)
Living situation
Alone
72 (21.2)
With family or partner
232 (68.2)
Other (eg, care home)
36 (10.6)
Employment status
Employed
88 (25.9)
Other
252 (74.1)
Marital status
Married or common law
168 (49.4)
Other
172 (50.6)
No. of preexisting comorbidities at injury
0.4±0.7
No. of health conditions
3.1±2.0
No. of experienced health conditions
0
17 (5.0)
1
57 (16.8)
2
65 (19.1)
≥3
201 (59.1)
FIM motor score
73.3±22.4
SF-36V2 PCS
35.1±10.7
SF-36V2 MCS
49.7±12.1
LiSat-11 score
4.1±0.9
NOTE. Values are n (%) or mean ± SD. Injury details (AIS grade and level) and sex at admission were from the RHSCIR inpatient data; all other variables were obtained from the community follow-up at 1 year (9–18mo) postdischarge to the community.
On average, participants in the modeling cohort reported 3.1±2.0 health conditions at the time of follow-up (see table 1): 17 (5.0%) reported none, 57 (16.8%) had 1, 65 (19.1%) had 2, and 201 (59.1%) had ≥3 (maximum, 15). The 5 most common were bodily pain (74.1%), spasticity (56.5%), urinary tract infections (35.0%), depression (26.2%), and high blood pressure (15.9%).
Path model results
Figure 3 represents the path model; only significant associations are represented with lines. The final model demonstrated a good fit (χ2=41.0, P=.38; comparative fit index=.997, Tucker-Lewis Index=.996, root mean square error of approximation=.010, standardized root mean square residual=.027), meaning that the results generated by the model closely matched the data. The model results include estimates and standardized estimates which indicate the measurable effect on the outcome of interest, with negative numbers meaning they lower the outcome and positive numbers meaning they increase the outcome value (eg, having 6 health conditions would decrease the FIM motor score by 9.18 [−1.53×6=9.18]) (table 3). R2 was .08 for health conditions, .49 for FIM, .31 for PCS, .07 for MCS, and .19 for LiSat-11.
Fig 3Proposed conceptual model (path model). Red arrows indicate a negative association; blue arrows, positive association; thickness of the arrow, magnitude of association. Layer descriptions are listed at the bottom of the figure. Only significant associations have been represented with lines. See table 3 for standardized estimates of association.
Factors associated with an increased number of health conditions included older age, higher BMI, and unemployment (see table 3). Comparatively, the number of health conditions was most related to being in or able to remain in employment, followed by age then BMI (see table 3).
Factors affecting function
Individuals who were older, with a cervical injury, a more severe injury, and more health conditions had a lower level of function (ie, a lower FIM motor score); living alone or with family (vs other, eg, care home) and employment were associated with higher function (see table 3). Severity of injury had the greatest effect on functional independence (see table 3).
Factors affecting HRQoL
Physical aspects of HRQoL as measured by the SF-36V2 PCS were negatively affected by injury severity, having less than high school education, and more health conditions, whereas employment and higher level of function (FIM motor score) increased self-reported physical health (see table 3). Comparatively, having a more severe injury (AIS grade B vs grade D) had the greatest effect on the PCS score, and education the least. The SF-36V2 MCS decreased with more health conditions and higher function (FIM motor score), with the number of health conditions having the greatest effect (see table 3).
Factors affecting life satisfaction
Overall, life satisfaction as assessed by the LiSat-11 was higher in those who were married and had a higher level of function (FIM motor score), whereas having more health conditions decreased life satisfaction (see table 3); comparatively, health conditions had the greatest effect, and functional independence the least (see table 3).
Discussion
In this article we have detailed the effect of health conditions on function, HRQoL, and life satisfaction using a path analysis. The most pervasive and persistent effect at each stage or layer of the path model is the number of health conditions, which not only has a negative effect on function, but also negatively effects other important patient outcomes in the model (eg, HRQoL, life satisfaction).
The most frequent health conditions reported in this study were bodily pain, spasticity, urinary tract infections,
The list of health conditions queried at follow-up is not exhaustive; therefore, common health conditions (eg, neurogenic bladder and bowel) will be underrepresented because they were not specifically queried.
We demonstrated that multimorbidity is common, with 59.1% of individuals experiencing ≥3 health conditions, and only 5.0% of our sample did not report any health conditions, consistent with the literature.
Our data show a clear association between multiple health conditions and decreased HRQoL. Others have also demonstrated an association of multimorbidity with a decrease in physical and mental HRQoL.
Future research into the effect of particular health conditions (eg, spasticity), their severity, and the effect of particular multimorbidity relations will further elucidate the effect on function, HRQoL, and life satisfaction. This work will help inform future health care management strategies such as a development of a screening tool to identify those most at risk of health conditions as previously suggested.
Development of intervention strategies (eg, helping individuals self-manage their SCI, tailoring clinical follow-up based on risk) could reduce the burden of health conditions and improve outcomes. Intervention strategies may prevent or lessen the severity of potentially modifiable health conditions (eg, pressure ulcers, urinary tract infections); however, other health conditions (eg, preinjury heart disease, cancer) are often not modifiable and must be managed.
An interesting finding of this work is that a higher motor function (FIM) score is positively associated with physical HRQoL (SF-36V2 PCS), but negatively associated with mental HRQoL (SF-36V2 MCS). Although a lower motor FIM score usually reflects a more severe injury and negatively affects reported physical health, a lower motor FIM score being associated with higher self-reported mental health is not so intuitive. This may represent a response shift (ie, process of adaptation after a change in health [SCI, the catalyst]), including changes in internal standards, values, and/or meaning with relation to evaluative outcomes.
and is therefore likely to apply in individuals with SCI. Individuals with more severe injuries may adjust to their injury more quickly than those with less severe injuries, who may continue to expect improvements longer term. Others have also suggested that response shift may be the reason for a reduced negative effect of health conditions on life satisfaction and participation in those who have lived longer with their injury.
Future research in this area could provide opportunities for clinicians to facilitate individuals with traumatic SCI in having a positive response shift, which would improve patient outcomes.
Study limitations
The model results apply only to the 340 of 580 individuals who met the study inclusion criteria and all available outcome data; outcome data were not imputed because there was sufficient sample size to use real-world data only. However, those excluded were similar, other than being slightly older (48.9 vs 41.6y). Because older individuals are more likely to experience health conditions, the effect of health conditions could be expected to be higher if all participants had complete data and were included in the analysis. Validation of our results in future work using a similar conceptual model could confirm reproducibility.
We included neurologic severity (AIS) and level of injury in the model separately; future analysis should consider neurologic impairment based on their combination because important clinical differences exist within AIS grades based on level of injury, and vice versa (eg, an individual with AIS grade A injury at the high cervical level may require ventilation and lack arm and hand function, whereas an individual with an AIS grade A injury in the low thoracic region would have normative respiratory function and use of their arms and hands). Although we included both AIS grade and level, the results of the model may be slightly different if we created a combined variable which included both (eg, AIS grade A C1-4).
The list of health conditions queried at follow-up is not exhaustive; many common health conditions not specified (eg, neurogenic bowel/bladder) will be underrepresented because few individuals provide other category responses. Therefore, the health conditions listed and their frequency may not be representative of the total burden of health conditions in individuals with SCI. However, reported function, HRQoL, and life satisfaction scores would remain as reported; only the number of health conditions would change if a more comprehensive collection of health condition data were performed.
Further, although the number of health conditions has been related to HRQoL and other outcomes, individual health conditions will differ in their effect, which may also be mitigated by successful treatment. For instance, pain may be reported, but some individuals may be receiving effective pain medication while others may not.
Future work addressing the level of effect of specific health conditions on health, HRQoL, and overall life satisfaction, and their interaction with other health conditions, is required.
Direct comparison of our results with other published studies is difficult because different measures are used to assess similar concepts; for example, the Sickness Impact Profile used in other studies
The Spinal Cord Independence Measure is now more commonly used than the FIM because it is SCI-specific. Consensus on how to operationalize the concepts in the ICF model through international standardization initiatives
would allow direct comparison of results. Results of this article are applicable at 9 to 18 months post-SCI; effects over time need exploration. Further, the health conditions assessed and follow-up time vary; others have assessed 8 health conditions
Finally, although identified relations are statistically significant, their clinical relevance must be assessed.
Conclusions
Complex interactions and enduring effects of health conditions post-SCI have a negative effect on function, HRQoL, and life satisfaction. Further, modeling the relations among these concepts will inform clinicians and service providers how to positively affect outcomes after SCI (eg, development of screening tools and protocols for managing individuals who have multiple health conditions).
Supplier
a.
Mplus version 7.11; Muthén & Muthén.
Acknowledgments
We thank the RHSCIR network and all the participating local RHSCIR sites: GF Strong Rehabilitation Centre, Vancouver General Hospital, Foothills Hospital, Glenrose Rehabilitation Hospital, Royal Alexandra Hospital, University of Alberta Hospital, Royal University Hospital, Saskatoon City Hospital, Winnipeg Health Sciences Centre, Toronto Western Hospital, Toronto Rehabilitation Institute, St. Michael's Hospital, Sunnybrook Health Sciences Centre, Hamilton General Hospital, Hamilton Health Sciences Regional Rehabilitation Centre, Victoria Hospital, University Hospital, Parkwood Hospital, The Ottawa Hospital Rehabilitation Centre, The Ottawa Hospital Civic Campus, Hôpital de l’Enfant Jésus, Institut de réadaptation en déficience physique de Québec, Centré de réadaptation Lucie-Bruneau, Institut de réadaptation Gingras-Lindsay-de-Montréal, Hôpital du Sacré Cœur de Montréal, Nova Scotia Rehabilitation Centre, QEII Health Sciences Centre, Saint John Regional Hospital, Stan Cassidy Centre for Rehabilitation, St. John's Health Sciences Centre, and L.A. Miller Rehabilitation Centre. We also thank Dilnur Kurban, MSc, and Tian Shen, MSc, for their assistance with analysis.
References
Jensen M.P.
Molton I.R.
Groah S.L.
et al.
Secondary health conditions in individuals aging with SCI: terminology, concepts and analytic approaches.
Field M.J. Jette A.M. Martin L. Workshop on disability in America, a new look: summary and background papers. National Academies Press,
Washington, DC2006
Validity of the Life Satisfaction Questions, the Life Satisfaction Questionnaire, and the Satisfaction With Life Scale in persons with spinal cord injury.
The Rick Hansen Spinal Cord Injury Registry and this work are supported by funding from the Rick Hansen Institute, Health Canada, Western Economic Diversification Canada, and the Governments of Alberta, British Columbia, Manitoba, and Ontario.
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