Risk of Mortality After Spinal Cord Injury: An 8-Year Prospective Study

Article Outline

• Abstract
• Purpose
• Methods
  • Participants
  • Data Collection Procedures
  • Measures
    • Biographic and injury characteristics
    • Environmental and psychologic characteristics
    • Behavioral characteristics
    • Health
  • Statistical Considerations
• Results
  • Participant Characteristics
  • Modeling
  • Final Model
• Discussion
  • Implications
  • Study Limitations
  • Future Research
• Conclusions
• References
• Copyright

Abstract 

Krause JS, Zhai Y, Saunders LL, Carter RE. Risk of mortality after spinal cord injury: an 8-year prospective study.

Objective

To evaluate a theoretical model for mortality after spinal cord injury (SCI) by sequentially analyzing 4 sets of risk factors in relation to mortality (ie, adding 1 set of factors to the regression equation at a time).

Design

Prospective cohort study of data collected in late 1997 and early 1998 with mortality status ascertained in December 2005. We evaluated the significance of 4 successive sets of predictors (biographic and injury, psychologic and environmental, behavioral, health and secondary conditions) using Cox proportional hazards modeling and built a full model based on the optimal predictors.

Setting

A specialty hospital.

Participants

Adults (N=1386) with traumatic SCI, at least 1 year postinjury, participated. There were 224 deaths. After eliminating cases with missing data, there were 1209 participants, with 179 deceased at follow-up.

Interventions

Not applicable.

Main Outcome Measures

Mortality status was determined using the National Death Index and the Social Security Death Index.

Results

The final model included 1 environmental variable (poverty), 2 behavioral factors (prescription medication use, binge drinking), and 4 health factors or secondary conditions (hospitalizations, fractures/amputations, surgeries for pressure ulcers, probable major depression).

Conclusions

The results supported the major premise of the theoretical model that risk factors are more important the more proximal they are in a theoretical chain of events leading to mortality. According to this model, mortality results from declining health, precipitated by high-risk behaviors. These findings may be used to target those who are at high risk for early mortality as well as to direct interventions to the particular risk factor.

Key Words: Health, Life expectancy, Mortality, Rehabilitation, Risk, Spinal cord injuries

List of Abbreviations: ASIA-D, Asia Spinal Injury Association, level of injury D, neurologically incomplete injury with motor sparing, BRFSS, Behavioral Risk Factor Surveillance System, HR, hazard ratio, LSQ-R, Life Situation Questionnaire-Revised, OAHMQ, Older Adult Health and Mood Questionnaire, SCI, spinal cord injury, VIF, variance inflation factor

SPINAL CORD INJURY continues to be associated with elevated risk of premature mortality.¹, ², ³ Because long-term mortality rates now appear to have stabilized,² it is more important than ever to understand the broad range of factors that contribute to premature mortality after SCI, beyond those simply related to biographic or injury factors, such as age or injury severity. Identifying these factors is the key to intervention.

Krause⁴ developed a theoretical risk model proposing 4 sequential stages of risk factors for mortality, beginning with basic biographic and injury factors. Although injury severity parameters are highly correlated with mortality, in the absence of consideration of age, recent research has suggested that they explain less than 2% of the variation in mortality and raise concordance rates by only about 6% above chance (50%).⁵, ⁶ Injury factors are stable and must be accounted for in any prediction to control for their effects statistically.

After consideration of biographic and injury factors, there are 3 additional sets of factors that serve as risk and protective factors for mortality: psychologic and environmental factors, behavioral factors, and health and secondary conditions. According to the model, these sets of factors are not of equal importance to mortality; rather, there is a hierarchical predictive chain. The more proximal the factor to mortality, the stronger the predictive influence. Health factors and secondary conditions are the most immediate predictors of mortality, followed by health behaviors, and then psychologic and environmental factors. The model suggests at least partial causation in that psychologic traits and environmental factors are believed to result in patterns of risk and protective behaviors, which in turn directly affect stability of health and the likelihood of secondary conditions. However, the model is not fully causal. Some variables may potentially have direct effects that are not mediated by other variables in the model. (Psychologic and environmental factors are distinct from each other but are introduced into the predictive model at the same point and are treated independently of each other in terms of prediction [ie, they have mutual influence with relatively equal importance in the prediction of mortality]). Applied to prediction, the model suggests that health factors would be the most important predictors of mortality, followed by behaviors, and then psychologic and environmental factors.

Recent research has demonstrated relationships between a wider array of predictive factors and mortality. For instance, a violent etiology of injury was a significant predictor of mortality in a study using data from the Model SCI Systems in the United States.² Two other studies using Model SCI Systems data identified more diverse predictors of mortality. In the first study,⁷ at least 1 variable from each level of the theoretical risk model was found to be predictive of mortality. Accounting for these variables led to substantial elevations in life expectancy under favorable circumstances. A more recent follow-up directly replicated this study,⁸ suggesting that life expectancy estimates might have been inflated as a result of instability of a single variable (workers compensation), but providing insufficient detail to assess utility of the theoretical risk model or to identify the significance of other types of nonbiographic and injury factors in relation to mortality.

Garshick et al⁹ identified 4 health risk factors for mortality, with 3 health status factors (diabetes, heart disease, reduced pulmonary function). They also found smoking, a behavioral factor, to be associated with mortality. Three risk factors were identified in a retrospective study of hospital records of all patients admitted to a Norwegian hospital between 1961 and 2002,¹⁰ including cardiovascular disease, substance or alcohol abuse, and psychiatric disorders. The investigators emphasized the role of prevention in promoting longevity, because the 3 factors identified provide a basis for identification of those at high risk and targets for interventions.

In a series of prospective studies using the theoretical risk model, sets of variables were systematically evaluated in relation to mortality while controlling for biographic and injury factors. In contrast with the current study, in which all sets of factors are evaluated simultaneously, each preliminary study focused on a single class of predictors (ie, health, behavioral, psychologic, environmental). Separate analysis of each class of predictors identifies those most important for building a full model across all variables (the purpose of the current study).

In the first study focusing on health factors and secondary conditions as predictors of mortality,⁶ 5 health factors were retained in the final model, including (1) surgeries for pressure ulcers, (2) depressive symptoms, (3) fractures/amputations, (4) symptoms of infections, and (5) hospitalizations. In an analysis of behavioral factors,⁵ there were 4 behavioral predictors significantly related to mortality: (1) smoking; (2) binge drinking; (3) prescription medication use for pain, spasticity, sleep, or depression; and (4) time spent out of bed (a protective factor). Three psychologic factors were identified as significant predictors of mortality: (1) sensation seeking, (2) neuroticism-anxiety, and (3) purpose in life (protective).¹¹ Last, 2 environmental variables were predictive of mortality: low income and social support (protective).¹²

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Purpose 

Our purpose was to conduct a prospective cohort study to build a model of mortality after SCI that sequentially included 4 sets of factors from the theoretical risk model of mortality.⁴ Therefore, in contrast with earlier studies that were restricted to evaluating only 1 level of the overall theoretical risk model (ie, psychologic, environmental, behavioral, or health), our purpose was to build a full model incorporating parameters from all levels. The sequential approach allowed us to enter sets of predictors in the order in which they appeared in the theoretical model. This was important because the model suggested that more distal predictors (those earlier in the predictive chain of factors) would become less important as additional factors were added. The strength of the association of psychologic and environmental factors should have been diminished as behavioral factors were added, then should have had negligible effects after the addition of health status and secondary conditions as predictors.

The unique contribution of this study is the simultaneous evaluation of all factors from the theoretical risk model, which allowed us to identify the most important predictors of mortality when considering all competing factors from each of the 4 general classes of predictors. Identifying the optimal predictors of mortality allowed us to predict who was at risk for early mortality more accurately. From a conceptual standpoint, we could evaluate the utility of the theoretical risk model by determining whether the most proximal predictors of mortality were indeed those retained in the final model.

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Methods 

Participants 

Participants were identified from a large specialty hospital in the Southeastern United States using 3 types of records: (1) the Model SCI Systems patient database, (2) the Model SCI Systems registry, and (3) an outpatient directory. All participants were adults with traumatic SCI of at least 1 year duration who had some residual neurologic impairment. Of the original cohort of 1929, 1386 participated (72%).

Data Collection Procedures 

Institutional Review Board approval was obtained prior to initiating the study. Letters were sent to all eligible participants describing the study and informing them that they would be receiving a questionnaire within the next few weeks. Two subsequent mailings were sent to nonrespondents. Follow-up phone calls were also made and additional materials sent if requested. Participants received $20 remuneration and were entered for drawings totaling $1500. Data collection occurred between July 1997 and April 1998. Mortality status was assessed as of December 31, 2005, using the National Death Index of the National Center for Health Statistics¹³ and the Social Security Death Index¹⁴ of the Social Security Administration. Participants who were not found deceased by either method were presumed to be alive.

Measures 

All factors were measured using a mail-in health survey. For space considerations, we have reported only the essential information on these measures, although more detailed descriptions (including psychometric characteristics) are reported elsewhere.⁵, ⁶, ¹¹, ¹²

Race was dichotomized as white and nonwhite. Age was measured at injury, and years lived with injury were calculated through the time of the survey. Injury level was categorized as cervical (C1–C4, C5–C8) and noncervical, and injury function was dichotomized (ambulatory, nonambulatory). This is a scheme that was used in the 4 preliminary studies.⁵, ⁶, ¹¹, ¹² This is similar to the scheme used from the Model SCI Systems,¹⁵ which uses 4 groups, 3 defined by ASIA grades A to C, with a single group denoting ASIA grade D (neurologically incomplete injury with motor sparing) regardless of injury level. We used ambulatory status in lieu of ASIA grades, which are not available, and broke ambulatory status down according to cervical and noncervical injuries (they are combined in the Model SCI Systems study).

Income levels were presented in the categories used in the BRFSS,¹⁶ a standardized instrument that is widely used by the Centers for Disease Control and Prevention. Because income was highly skewed, an indicator variable was created to represent low income (<$20,000). Income was based on all sources from all members of the household, rather than the participant's earnings alone.

The Reciprocal Social Support Scale¹⁷ was used to measure social support. Participants answered 8 questions on a 7-point scale (1=never; 7=always) rating type of support received from their families, friends, and community. They were also asked about the frequency with which upsetting things happened between them and their family, friends, or community. We used the total social support scale and the upset score.

The Zuckerman Kuhlman Personality Questionnaire¹⁸ is a 99-item measure of personality that generates information on 5 scales. These scales are Impulsive Sensation Seeking, Neuroticism-Anxiety, Aggression-Hostility, Sociability, and Activity. We used 2 of these scales (Impulsive Sensation Seeking and Neuroticism-Anxiety). Impulsive Sensation Seeking was designed to measure a lack of planning and the tendency to act impulsively and served as a proxy for reckless and dangerous behavior. Neuroticism-Anxiety measures tension, worry, and fearfulness.

The Purpose in Life Scale¹⁹ was developed from a humanistic perspective by measuring the degree to which a person perceives themselves as finding meaning in life. It consists of 20 statements rated on a 7-point scale. Scores range from 20 to 140.

We used core portions of the BRFSS to measure alcohol behaviors and smoking behaviors. Binge drinking was defined as the number of occasions in the past month the participant reported consuming 5 or more drinks. In contrast, a composite score was developed for 3 smoking items that assessed a participant's smoking behaviors. The first 2 items assessed whether the participant had ever smoked on a regular basis or whether the participant currently smoked in bed using either (1) no or (2) yes as the response categories. The number of cigarettes a day currently smoked was assessed as (1) none at all, (2) 1 to 9 (less than half a pack), (3) 10 to 19 (<1 pack), (4) 20 to 40 (1–2 packs), or (5) more than 41 (more than 2 packs). The sum of these items was used to indicate higher smoking risk behaviors (standardized Cronbach α=.76).⁵

The Spinal Cord Injury Health Survey²⁰ measures prescription medication usage—how frequently participants use prescription medications that may have psychotropic effects. Participants were asked how frequently they used medications for pain, spasticity, depression, and sleep. Each item had 4 response categories: never, sometimes, weekly, and daily. A composite score was constructed as simple summated rating scales of the 4 items (standardized Cronbach α=.68), and a higher score indicated a higher use of medications. Last, a single item reflecting the number of hours out of bed during the day was used as a general activity indicator. This is a widely used indicator of activity and is in the Craig Handicap Assessment and Reporting Technique.²¹

Several instruments were used to measure the health factors and secondary conditions, including items from the LSQ-R²², ²³ and the OAHMQ.²⁴ Selected items were also developed for the study. Chronic diseases, such as heart disease, diabetes, and pulmonary function, are important factors to consider in relation to mortality, because they are preventable and have been identified in previous research. However, we did not assess these conditions in the current study because they require a diagnosis by a physician, and we were concerned that they would not be reported accurately. There are similar restrictions in measuring conditions such as urinary tract infections. Therefore, our assessment focused on conditions that are more accurately reported in self-assessment, such as hospitalizations and injuries, as well as symptoms that may result from a condition (eg, amputations may relate to diabetes; fractures may relate to osteoporosis; sweats, chills, and fevers may relate to urinary tract infections).

The number of days hospitalized over the previous 12 months was used from the LSQ-R.²², ²³ Several types of secondary conditions were assessed, including the number of symptoms of infection like fevers, sweats and chills, and urinary tract infections (number of occurrences of each of these in the past year using ordinal rankings: 0, 1–2, 3–6, 7–12, ≥13). A summary measure (summated score) was created. Another item reflected whether participants had ever had either an amputation or extremity fracture. Pressure ulcers were defined as “open sores in pressure areas, such as your tailbone, ischium, heel, elbows.” Participants were asked to indicate the number of surgeries to heal pressure ulcers since SCI onset.²⁰

The 22-item OAHMQ²⁴ was used to measure depressive symptoms. Scores of 11 or higher were considered to indicate probable major depression.

Statistical Considerations 

A 3-stage hierarchical model building strategy was employed to identify the association of psychologic, environmental, behavioral, and health factors with mortality and determine an optimal set of predictors of mortality. Independent variables within each factor were selected based on results from the previous studies.⁵, ⁶, ¹¹, ¹² Cox proportional hazards modeling was used, with the number of days between the survey and event (ie, mortality) as the dependent variable.

During the first stage of analysis, a base model consisting of biographic (sex, race, age at injury, and years lived since injury) and injury characteristics (functional injury classification) was specified.

The second stage of the analysis focused on adding single variables to the base model as a means of screening potential predictors for the final stage model. All variables significant at the α equal to .15 level were considered for subsequent modeling. Variables that passed through the initial screening process were then put together to assess for multicollinearity. No VIF was larger than 10, and no tolerance was smaller than 1 for all variables, indicating no multicollinearity.²⁵

In proceeding to the final model, variables retained from the second stage screening were added to the base model sequentially as a group by factor. Three intervening models were generated based on the theoretical risk model.⁴ The first intervening model added the psychologic and environmental factors simultaneously to the base model. The second intervening model added the behavioral factors to the first intervening model. The health factors were added to the second intervening model to create the third intervening model. At each intervening model, backward elimination, with α equal to .15 as the significance level, was employed to select the optimal set of predictors.

The final stage of the analysis formulated a model that consisted of all variables retained from the third intervening model with significance at .05. Once the final model was established, all pairwise interaction terms were included to assess goodness of fit further. A Wald linear contrast indicated no interaction item was needed (P=.4); therefore, all interaction items were removed. The proportional hazards assumption of the final model was tested using the Schoenfeld²⁶ residuals and found to be tenable (global test, P=.75). The fit of the model was assessed using the Nagelkerke²⁷ pseudo-R and the C-statistic.²⁸, ²⁹, ³⁰ The former is a comparative fit index that can be used to assess the strength of the model fit, and the latter is interpretable as the probability that the cases (ie, deaths) have higher risks as measured by the linear component of the regression model. Accordingly, a value of 0.5 is used for chance prediction, and the discrimination of the model is improved as the C-value approaches 1.0; however, both indices, like most comparative fit indices, have limited generalizability beyond the dataset at hand.

Model building and calculation of VIF, tolerance, and the Nagelkerke²⁷ pseudo-R² were conducted using SAS System version 9.1.3.^a Validation of the proportional hazards assumption and estimation of the C-statistic were performed using STATA version 10.0.^b

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Results 

Participant Characteristics 

There were 1209 participants included in the final statistical model, of which 179 (14.8%) died. Participants' mean age at injury was 31.8 (SD=14.0), with an average of 8.9 (SD=6.9) years since their injury (table 1). Overall, 54.4% had cervical injuries, and 21.1% of participants were ambulatory. Most participants were white (74.8%) and were men (74.0%).

Table 1. Sample Description

Variable	Raw Dataset
	N	Percent
Injury classification
C1–C4, nonambulatory	175	13.16
C5–C8, nonambulatory	406	30.53
Noncervical, nonambulatory	468	35.19
Cervical, ambulatory	142	10.68
Noncervical, ambulatory	139	10.45
Biographic
White	1032	74.84
Men	1026	74.03
Age at injury (y)	31.83±13.99
Years since injury	8.86±6.86
Psychologic factors
Sensation seeking	4.35±2.75
Neuroticism-anxiety	3.56±2.41
Purpose in life	99.02±21.29
Environmental factors
Low income (<$20,000)	684	51.47
Social support	17.54±3.89
Social upset	7.50±3.61
Behavioral factors
Prescription medication use	7.61±3.38
Number of binge-drinking days	1.24±3.87
Out-of-bed hours	12.73±3.89
Smoking composite score	4.27±1.69
Health factors
Days in the hospital	4.34±8.66
Number of infection symptoms	9.80±9.64
Fracture/amputation	31	2.24
Surgeries to repair ulcers	0.55±1.54
Probable major depression	339	24.53

NOTE. Values are mean ± SD or as otherwise indicated.

Comparison of those with and without missing data (table 2) indicated that deceased cases were disproportionately represented among those with missing data (25.1% of the deceased had missing data compared with 14.8% of the survivors; P<.001). Significant differences were also observed for race, because 69.3% of those with missing data were white compared with 75.8% of those with complete data (P<.05). Similarly for sex, 66.7% of those with missing data were men compared with 74.9% of those with complete data (P<.05). The average age of onset of those with missing data was 35.4 compared with 31.3 for those without missing data (P<.001). There were no differences as a function of injury severity or duration of SCI.

Table 2. Comparison of Those With and Without Missing Data

Participants Variable	Complete Data		Missing Data		P of χ2
	N=1209	Percent	N=177	Percent
Number of deaths	179	14.8	45	25.1	.0003
Injury classification
C1–C4, nonambulatory	160	13.2	15	13.2	.80
C5–C8, nonambulatory	368	30.4	36	31.6	.83
Noncervical, nonambulatory	423	35	41	36	.63
Cervical, ambulatory	131	10.8	10	8.77	.55
Noncervical, ambulatory	127	10.5	12	10.5	.12
Biographic
White	917	75.8	79	69.3	.02
Men	906	74.9	76	66.7	.04
Age at injury (y)	31.34±13.33		35.41±17.76		.0005⁎
Years since injury	8.90±6.96		8.54±6.15		.52⁎

NOTE. Values are mean ± SD or as otherwise indicated.

Modeling 

Variables were entered in accordance with the theoretical risk model (table 3). The base model represents only biographic and injury characteristics. When adding the psychologic and environmental factors (Intervening 1), biographic and injury characteristics, which were significant in the base model, remained significant. Low income was the only statistically significant environmental factor, and sensation seeking and purpose in life were significant psychologic factors.

Table 3. Intervening Models

Variable	Base⁎		Intervening 1†		Intervening 2‡		Intervening 3§
	HR	P	HR	P	HR	P	HR	P
Injury classification
C1–C4, nonambulatory	4.83	<.0001	5.04	<.0001	3.20	.004	3.41	.001
C5–C8, nonambulatory	3.13	.002	2.73	.01	2.10	.06	2.22	.03
Noncervical, nonambulatory	3.41	.001	2.89	.01	2.22	.04	2.05	.05
Cervical, ambulatory	1.16	.74	0.95	.91	0.85	.74	1.03	.94
Noncervical, ambulatory (referent)	1.00	NS	1.00	NS	1.00	NS	1.00	NS
Biographic
White	0.91	.57	1.23	.29	1.31	.20	1.13	.50
Men	1.12	.51	1.12	.55	1.14	.51	1.18	.37
Age at injury	1.06	<.0001	1.06	<.0001	1.06	<.0001	1.06	<.0001
Years since injury	1.05	<.0001	1.06	<.0001	1.06	<.0001	1.05	<.0001
Psychologic factors
Sensation seeking			1.05	.08	1.05	.10	NS	NS
Neuroticism-anxiety			1.06	.11	NS	NS	NS	NS
Purpose in life			0.99	.01	0.99	.05	NS	NS
Environmental factors
Low income (<$20,000)			1.64	.01	1.44	.05	1.41	.04
Social support			NS	NS	NS	NS	NS	NS
Social upset			NS	NS	NS	NS	NS	NS
Behavioral factors
Prescription medication use					1.08	.001	1.06	.004
Number of binge-drinking days					1.04	.01	1.04	.02
Out-of-bed hours					0.94	.004	NS	NS
Smoking composite score					1.08	.10	NS	NS
Health factors
Days in the hospital							1.02	.03
Number of infection symptoms							NS	NS
Fracture/amputation							2.89	.0006
Surgeries to repair ulcers							1.13	.0003
Probable major depression							1.78	.0003

Abbreviation: NS, not significant; variable not significant and removed from the model.

Behavioral factors were added next in the Intervening 2 model. The addition of behavioral factors did not affect the previously entered factors, and all 4 behavioral predictors were significant in addition to those variables already in the equation.

Health factors, added in the Intervening 3 model, had the greatest effect on other factors. Previously significant biographic and injury characteristics remained, as did low income (environmental); however, as expected in the theoretical risk model, all psychologic variables lost significance. Two behavioral factors also became nonsignificant (out-of-bed hours, smoking composite score), with binge drinking and prescription medication use remaining significant.

Final Model 

In the final model, injury classification remained significantly associated with mortality, where the hazard of mortality increased for each increase in injury level among persons who were not able to ambulate (table 4). Age at injury (HR=1.06; P<.0001) and years since injury (HR=1.05; P<.0001) were the only biographic factors related to mortality. One environmental factor was significant: persons with low income had a 41% increased hazard of mortality (HR=1.41; P=.04). Two behavioral factors were retained: prescription medications and number of binge drinking days. Last, all health factors except 1 (number of infectious symptoms) were significantly associated with mortality. Having a fracture or amputation was most strongly associated with mortality (HR=2.89; P=.0006). Also, persons with probable major depression had a 78% increased hazard of mortality (HR=1.78; P=.0003). A standardized HR was reported for the number of surgeries to repair ulcers, and for a 1.0 SD increase, the hazard of mortality increased 21%. For every 8.7 days hospitalized (1 SD), the hazard of mortality increased 16.7%.

Table 4. Final Cox Proportional Hazards Model for Predicting Mortality After SCI

Variable	Adjusted Model†		Final Model⁎
	HR	P	HR‡	95% CI		P	Standardized HR§
Injury classification
C1–C4, nonambulatory	NA		3.41	1.62	7.18	.001	NS
C5–C8, nonambulatory	NA		2.22	1.08	4.54	.03	NS
Noncervical, nonambulatory	NA		2.05	1.01	4.18	.05	NS
Cervical, ambulatory	NA		1.03	0.43	2.47	.94	NS
Noncervical, ambulatory (referent)			1.00	1.00	—	—	NS
Biographic
White	NA		1.13	0.79	1.61	.50	NS
Men	NA		1.18	0.82	1.69	.37	NS
Age at injury	NA		1.06	1.05	1.07	<.0001	2.20
Years since injury	NA		1.05	1.03	1.07	<.0001	1.38
Psychologic factors
Sensation seeking	1.12	<.0001	NS	NS	NS	NS	NS
Neuroticism-anxiety	1.05	.10	NS	NS	NS	NS	NS
Purpose in life	0.99	<.0001	NS	NS	NS	NS	NS
Environmental factors
Low income (<$20,000)	1.93	<.0001	1.41	1.01	1.97	.04	NS
Social support	0.95	.003	NS	NS	NS	NS	NS
Social upset	1.02	.22	NS	NS	NS	NS	NS
Behavioral factors
Prescription medication use	1.10	<.0001	1.06	1.02	1.11	.004	1.23
Number of binge-drinking days	1.04	.005	1.04	1.01	1.07	.02	1.15
Out-of-bed hours	0.92	<.0001	NS	NS	NS	NS	NS
Smoking composite score	1.19	<.0001	NS	NS	NS	NS	NS
Health factors
Days in the hospital	1.03	<.0001	1.02	1.00	1.03	.03	1.16
Number of infection symptoms	1.03	<.0001	NS	NS	NS	NS	NS
Fracture/amputation	3.69	<.0001	2.89	1.57	5.29	.0006	NS
Surgeries to repair ulcers	1.20	<.0001	1.13	1.06	1.21	.0003	1.21
Probable major depression	2.21	<.0001	1.78	1.30	2.45	.0003	NS

Abbreviation: NS, not significant; variable not significant and removed from the model.

With the addition of each set of factors, both the pseudo-R² and the C-statistic increased (table 5). The pseudo-R² increased from .121 to .179 from the base to the final model, with the largest increase after the addition of behavioral factors (from .136 to .161). The C-statistic changed from .730 in the base model to .784 in the final model.

Table 5. Model Fit Statistics

NOTE. 1. Five category breakdown (C1–C4, C5–C8, noncervical, and so forth).

2. Add core biographic variables includes white, men, age at injury, and years since injury.

3. Add psychologic and environmental factors to the base model.

4. Add behavior factors to Intervening model [1].

5. The final model as identified in Table 3.

6. The maximum model consisted of all variables of interest in addition to the base model.

Abbreviation: ND, no data to enter into cell.

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Discussion 

These results provide relatively strong support for the theoretical risk model and the need to consider multiple sets of risk and protective factors in relation to mortality. First, as each set of risk factors was entered into the equation, those factors more distal to mortality were most likely to become nonsignificant. For example, when health factors were introduced, psychologic factors were no longer significant. This is consistent with the basic mediating hypothesis that each successive set of factors mediates the relationship between the previous factors and subsequent factors, ultimately ending in mortality.

Second, the model fit improved with the addition of each set of factors, including health factors that were entered last. The fact that several health predictors were retained in the final model and the model improved after their inclusion is a testament to their importance in mediating the relationship between other predictors and mortality.

There were some inconsistencies, however. Specifically, low income, binge drinking, and prescription medication use were retained in the final model. This suggests that these are very powerful predictors of the model that were not mediated by health conditions in the current study. This may be because they have an independent effect or contribution to mortality (contrary to the model), or this may reflect the limited scope of measurement of health conditions. Had additional health factors been included, these variables might no longer have been significant.

Implications 

Ultimately, the importance of any study can be gauged by the extent to which it helps to lead to actual changes in outcomes (in this case, increased longevity). In contrast with biographic and injury factors that are not modifiable, the risk and protective factors identified in the current study present opportunities for identification of those at high risk for premature mortality and targets for interventions. Because the theoretical risk model is sequential in nature, with a series of mediating relationships, interventions targeted at the earliest sets of variables (psychologic and environmental) may have the greatest promise for early prevention. Intervening at later stages of risk (eg, after the development of specific health conditions) is less likely to be successful. The more proximal the risk factor to mortality, the greater the strength of prediction, and the more difficult it will be to intervene to extend longevity.

There are many ways the current findings can be translated into clinical practices to reduce early mortality. First, although it is not surprising that any of these particular risk factors are associated with early mortality, the identification of the optimal set of predictors will allow clinicians from multiple disciplines to assess risk for mortality quickly and efficiently. A minimum intervention for any clinician is to share with the patient who has SCI what factors increase or decrease the risk for mortality. This alone will empower the patient.

Second, clinicians may use the information on the specific risk factors to develop interventions in their own area of expertise. For instance, the finding that a depressive disorder was significantly related to mortality is an indicator of the importance of routinely assessing for depressive disorders and treating wherever possible. Rehabilitation psychologists should be intricately involved with this process to ensure that assessments are routine, including at outpatient visits, and that those at high risk for depression are identified and appropriate follow-up is implemented. Although our findings do not indicate causality, physicians need to be aware of the relationship between psychotropic prescription medication use and mortality and be cautious when prescribing these medications, particularly multiple medications for different symptoms (ie, pain, spasticity, sleep, depression).

A third way to use these findings is to develop a systems approach to treatment for SCI that includes high-risk areas, such as alcohol abuse. Factors including tobacco use and sensation seeking were not significant in the final model, indicating that they did not contribute in a unique variance to the predictive model, but each has been previously identified as a risk factor for mortality. Despite the importance of behavioral factors on mortality and other important health outcomes,²⁰, ³¹ rehabilitation programs rarely implement any types of intervention for tobacco or substance misuse. If we are to intervene successfully to reduce early mortality and increase longevity, we must aggressively promote healthier behaviors and improvement of overall health.

Study Limitations 

The primary limitations include (1) left censoring of the data (sample is drawn from some point after inception—the time of the SCI); (2) absence of participants who experienced mortality within the first year after injury, when mortality is highest; (3) potential influence of missing data on estimating life expectancy and the strength of predictors; (4) time between prospective data collection and determination of mortality; (5) use of self-report data; (6) use of proxy variables for classification of injury severity; (7) inclusion of only a subset of risk and protective factors; and (8) the nature of the statistics.

Whereas the first 3 limitations (1–3) applied broadly to the study and have been discussed elsewhere,⁵, ⁶ the other methodological limitations are important to discuss. First, the 8-year interval between collection of the prospective data and determination of mortality limits the power of the study, because some of the predictor variables may change over time. Conversely, although increasing the window may weaken predictors, extending the prediction beyond 8 years would also be of great value. Second, we use self-report measures of health outcomes, whereas collecting this data through clinical assessments would have been preferable. However, it simply is not economically feasible to perform the number of clinical assessments necessary in epidemiologic studies of mortality, so use of self-report is a necessary trade-off. Similarly, injury status was determined by self-report rather than clinical examination, so ambulatory status essentially serves as a proxy variable for neurologically incomplete injury with motor sparing (ie, ASIA D). Although this study included very diverse predictors from each component of the theoretical risk model, the specific risk and protective factors used represent only a subset of all possible variables. This could account, but does not necessarily account, for the finding that 1 environmental and 2 behavioral variables remained significant after the addition of health factors. Granting agencies and institutional review boards attempt to minimize participant burden in self-report studies, and this places constraints on the number and diversity of variables that can be included in any study. Finally, the interpretation of the model fit indices (pseudo-R² and C-statistic) has limited generalizability beyond this data.

Future Research 

Ongoing research is needed to incorporate more frequent assessments of risk and protective factors and to incorporate additional parameters, including biomarkers of stress, age, and vascular health. These assessments should take place over intervals longer than 8 years. Quality-of-life indicators also need to be incorporated into the model, because they were among the first factors to be identified in association with mortality.³², ³³, ³⁴ Additional research is needed to identify the underlying mechanisms for the observed findings, such as the heightened risk of mortality related to fractures and amputations. We currently do not know whether these factors were significant by virtue of being indicators of overall health, or whether the physiologic processes related to fractures or amputations actually contribute to the early mortality. Last, investigation of an expanded set of risk and protective factors in association with causes of death would dramatically enhance our understanding of premature mortality and guide intervention strategies to promote greater longevity.

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Conclusions 

Using a prospective cohort design guided by a theoretical risk model, we identified an optimal set of predictors of mortality that included 1 environmental factor (income), 2 behavioral factors (binge drinking, psychotropic prescription medication use), and 4 health factors (hospitalizations, fractures/amputations, surgeries for pressure ulcers, probable major depression). Assessing these constructs in clinical settings will identify persons at high risk for premature mortality as well as provide targets for prevention strategies.

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• a SAS System for Windows, Version 9.2.; SAS Institute Inc, 100 SAS Campus Dr, Cary, NC 27513-2414.
• b STATA for Windows, Version 10.0.; StataCorp LP, 4905 Lakeway Dr, College Station, TX 77845.