NIH Public Access
Author Manuscript
Arch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

NIH-PA Author Manuscript

Published in final edited form as:Arch Phys Med Rehabil. 2011 November ; 92(11): 1770–1775. doi:10.1016/j.apmr.2011.05.024.

Health, Secondary Conditions, and Life Expectancy after Spinal
Cord Injury
James S. Krause, PhD1 and Lee L. Saunders, PhD1
1Department of Health Sciences and Research, College of Health Professions, Medical Universityof South Carolina, Charleston, SC

Abstract
Objective—To evaluate the association of health status, secondary health conditions,
hospitalizations, and risk of mortality and life expectancy (LE) after spinal cord injury (SCI).
Design—Prospective cohort study.

NIH-PA Author Manuscript

Setting—Preliminary data were collected from a specialty hospital in the Southeastern UnitedStates, with mortality follow-up and data analysis conducted at a medical university.
Participants—A total of 1361 adults with traumatic SCI, all at least 1 year post-injury at thetime of assessment, were enrolled in the study. There were 325 deaths. After elimination of thosewith missing data on key variables, there were 267 deaths and 12,032 person-years.
Interventions—None
Main Outcome Measures—Mortality status was determined by routine follow-up using the
National Death Index through December 31, 2008. A logistic regression model was developed toestimate the probability of dying in any given year using person years.

NIH-PA Author Manuscript

Results—A history of chronic pressure ulcers, amputations, a depressive disorder, symptoms ofinfections, and being hospitalized within the past year were all predictive of mortality. LE
estimates were generated using the example of a male with non-cervical, non-ambulatory SCI.
Using 3 age examples (20, 40, 60), the greatest estimated lost LE was associated with chronicpressure ulcers (50.3%), followed by amputations (35.4%), 1 or more recent hospitalizations(18.5%), and the diagnosis of probable major depression (18%). Symptoms of infections was
associated with a 6.7% reduction in LE for a 1 standard deviation increase in infectious symptoms.
Conclusion—Several secondary health conditions represent risk factors for mortality anddiminish LE after SCI. The presence of 1 or more of these factors should be taken as an indicatorof the need for intervention.
Keywords
spinal cord injury; mortality; risk; health; economics; life expectancy© 2011 The American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.
Correspondence to: James S. Krause, PhD, Department of Health Sciences and Research, College of Health Professions, Medical
University of South Carolina, 77 President St, C101, MSC 700, Charleston, SC 29425, Phone: 843-792-1337, krause@musc.edu Fax:
843-792-5649.
Reprints are not available from this author.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to ourcustomers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of
the resulting proof before it is published in its final citable form. Please note that during the production process errors may bediscovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Krause and Saunders

Page 2

NIH-PA Author Manuscript

Research over the past 3 decades has documented diminished LE after the onset of SCI, with
LE inversely proportional to injury severity.1–3 Although LE has decreased during the firstyear post-injury, it appears stable after the first year post-injury.2–4 Since medical
management is no longer improving long-term survival rates, epidemiologic studies areneeded to identify risk factors for premature mortality, allowing professionals to apply
proven intervention strategies to enhance LE. These studies will be most successful when
they systematically address multiple risk factors, allowing for diverse intervention strategies.

NIH-PA Author Manuscript

Krause5 developed a theoretical risk model [1996] to guide investigations of prediction of
LE by accounting for multiple sets of predictive factors including biographic and injury,
psychological, socio-environmental, behavioral, and health variables. According to themodel, health factors are the most immediate and most important predictors of LE. Healthfactors may be specific secondary conditions, such as pressure ulcers, or more generalindicators of a history of treatment (e.g., hospitalization).6 They mediate the relationships
between health behaviors,7 such as smoking and alcohol misuse, with LE. Psychologicalfactors,8 such as personality characteristics, and socio-environmental factors,9 such as
poverty or social support, have less direct impact on LE according to the model. Studies ofhealth factors and mortality after SCI are rare, and findings are not typically translated into
differential LE. Studies of causes of death have indicated heart disease, external causes, andrespiratory complications as the leading causes of death after the first year post-injury,2,10
but they do not fully account for the health factors that may lead to diminished LE. Utilizing
data from the NSCISC, a network of facilities in the USA contributing data to a common
database, several health factors were identified as risk factors including self-rated health,hospitalizations, and having a grade 3 or 4 PU.4 Another study found having heart disease
was predictive of mortality.11 Although other chronic diseases, including diabetes,
hypertension, chronic obstructive pulmonary disease, and asthma were not significant
predictors of mortality, the sample was small (n=361, 37 deaths). These studies are limited
to mostly basic demographic and injury characteristics that do not help clarify the role ofdiverse sets of variables in relation to mortality and LE.
In the first of 2 analyses of data from prospective cohort studies, 5 health factors weresignificantly related to mortality after controlling for demographic and injury characteristics
among a sample of 1265 participants over a 7- to 8-year interval (188 deaths).6 These
included days spent in the hospital, amputations or fractures, infections, surgeries for PUs,and PMD. In the second analysis systematically evaluating all components of the fulltheoretical risk model, 4 health predictors were retained in the model (all except infectious
symptoms), along with 2 behavioral factors (prescription medication use, binge drinkingdays) and 1 environmental factor (low income).12

NIH-PA Author Manuscript

Only a handful of studies examined LE directly, nearly all of which have used NSCISC data.
DeVivo et al.2 reported LE for various ages and injury levels and found the percentage of
LE lost ranged from 11.8% for a 20 year old male with a Frankel D injury to 69.5% for asame aged male who was also ventilator dependent. Krause, DeVivo, and Jackson4
subsequently evaluated a broader range of predictors of mortality and LE. Under favorable
health assumptions alone, LE was 56.4% for a person age 50 and 64.8% for a person age 25.
Although health factors were considered, 2 socioeconomic factors, workers compensation
insurance and the economic subscale from the CHART, were the primary factors associatedwith greater longevity. Strauss et al.13 replicated this analysis after the accrual of time andmore deaths, finding less substantial effects of economic factors on LE (workers
compensation was no longer significant). However, when using actual family income, rather
than the CHART subscale, Krause Saunders, and DeVivo14 found much more substantial
effects of economic factors on LE. Two additional studies utilized non-NSCISC data,

Arch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

Krause and Saunders

Page 3

NIH-PA Author Manuscript

demonstrating again the importance of familial income15 and risk behaviors with LE.16 For
instance, after controlling for demographic and injury characteristics, smoking, binge
drinking, use of prescription medication, and less days out of the house were significantly
associated with decreases in LE.16

Summary and Purpose
The existing literature, both conceptual and empirical, clearly implicates secondary healthconditions as elevating risk of mortality. By definition, these factors would also lead todiminished LE, although these relationships have never been quantified. However, LE has
infrequently been reported in the literature and, with 2 exceptions, has been restricted toanalysis of NSCISC data. Our purpose was to evaluate the relationship of secondary healthconditions and hospitalizations with LE after SCI. All analyses follow the theoretical risk
model, which has been the focus of recent research.5 The first set of risk factors within thetheoretical risk model (demographic and injury characteristics including age, sex, injury
severity) are used as statistical controls, with the primary focus on health factors which are
the most important predictors within the risk model. Therefore, health factors are evaluatedafter controlling for demographic and injury characteristics.

Methods
NIH-PA Author Manuscript

Participants
After receiving Institutional Review Board approval, participants were identified fromrecords of a specialty hospital in the Southeastern United States. There were 3 inclusion
criteria: (1) traumatic SCI with some residual impairment, (2) 18 years or older, and (3)minimum of 12 months post-injury. Of 1929 potential participants, 1386 (72%) completed
an assessment related to health and secondary conditions. We excluded 25 participants withquestionable diagnoses or no date of injury or age, leaving 1,361 for analysis. Figure 1
summarizes participant inclusion in the final analysis.
Procedures

NIH-PA Author Manuscript

Cover letters were sent to 1929 potential participants to describe the study and explain
informed consent. Materials were sent 4 to 6 weeks later. Non-respondents received 2
follow-up mailings and a follow-up phone call. Additional materials were sent if requestedby the participant. Participants (n=1386, 72% response rate) received $20 remuneration, anddrawings were conducted totaling $1500. Prospective data were collected between July 1997
and April 1998. Mortality status was assessed as of December 31, 2008, the most recent
available data using the NDI.17 Participants who were not found deceased were presumed to
be alive as of that date.
Variables and Measures
Demographic and injury variables were included as statistical controls, including sex, age atinjury, years since injury, and injury severity. Injury severity was classified into 4 categories
using a scheme identical to that used in preliminary studies6–9,12 and similar to the
NSCISC.4 All non-ambulatory participants were classified according to 3 neurologic levels(C1–C4, C5–C8, non-cervical), whereas ambulatory participants were grouped together
regardless of neurologic level (ambulatory status is a proxy for ASIA D).
We measured 5 indicators of health and secondary conditions, including: (a) PU history, (b)PMD (yes/no), (c) symptoms of infections, (d) amputations, and (e) hospitalization. The
OAHMQ was used to measure PMD.18 It is a 22-item measure which was standardized suchthat scores above the cutoff of 11 indicate PMD. The OAHMQ has been used in previous

Arch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

Krause and Saunders

Page 4

NIH-PA Author Manuscript

studies to assess PMD among community dwelling participants with SCI.19 Kemp andAdams31 reported a test-retest coefficient of .87 (p < .001) and alphas were .93 (p < .001). In
terms of validity, sensitivity and specificity were reported to be .93 and .87, respectively. A
recent study comparing the OAHMQ to the abbreviated version of the Patient Health
Questionnaire indicated the OAHMQ produces modestly higher rates of depression.20
Symptoms of infection included fevers, sweats and chills, and urinary tract infections in the
12 months prior to the study. Participants were asked the number of occurrences (0, 1–2, 3–
6, 7–12, ≥13). A summary measure (summated score) was created similar to that previously
reported in the literature.6 Amputations were defined by having 1 or more upper or lower
extremity amputations since SCI onset. Hospitalizations within the last year weredichotomized. PUs was defined as “open sores in pressure areas, such as your tailbone,ischium, heel, elbows.” PU history was self-classified as: 1) never had PU, 2) only
immediately after injury, but rarely since, 3) every couple of years, 4) at least 1 PU per year,
and 5) always seem to have PU. For data analysis, we collapsed the middle 3 responses
leaving: 1) no history of PU; 2) non-chronic PU (categories 2, 3, 4); and 3) chronic PU.
Statistical Considerations

NIH-PA Author Manuscript

We used a 2 stage hierarchical model building strategy with a logistic regression model andmortality as our outcome. Person-year data was constructed using the time from survey tomortality or to censoring (December 31, 2008). This method treats each follow-up year foreach person as a separate observation.21 These methods have been used in previous
mortality analyses,4,22 and logistic regression was conducted on the person-year dataset,with mortality as the outcome. This method allows the researcher to take into accountvarying covariates across time in the calculation of life expectancy.

NIH-PA Author Manuscript

The first analysis included only demographic and injury characteristics as a baseline model.
In stage 2, health variables were added. PU history (reference=none), PMD (reference=no),hospitalizations (reference=0), and amputation (reference=no) were entered as categoricalvariables and infectious symptoms as a continuous variable. Symptoms of infection was
standardized, and the odds of mortality for 1 standard deviation were reported. HosmerLemeshow and global χ2 tests were used to assess goodness-of-fit of the model.23 The Cstatistic, measuring area under the Receiver Operating Characteristic curve, was used toassess discriminatory ability.23 A value of 0.5 for the C-statistic represents no better thanchance prediction, and C-statistic values approaching 1 represent improved discrimination of
the model. ORs with 95% CIs were calculated. We calculated differences in LE bycalculating the probability of survival for each year of life through age 100 using methods
widely reported in the literature.15,16,21,24 When calculating life expectancy for different
health conditions, all other health outcomes were held to their reference level (i.e. whenassessing life expectancy for PMD, all other health conditions were kept at their reference
level – specified above).

Results
Participant Characteristics
There were 194 participants (14.3%) with missing data on at least 1 variable. Deceased
cases were disproportionately represented among those with missing data (17.9% of thedeceased had missing data compared with 13.1% of the survivors; p=<.05). Significant
differences were also observed for race, as 63.9% of those with missing data were white
compared with 76.9% of those with complete data (p<.05). The average age of onset of
those with missing data (34.8) was greater than those without missing data (31.3, p<.001).

Arch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

Krause and Saunders

Page 5

NIH-PA Author Manuscript

There were no differences as a function of sex, injury severity, or injury duration. There
were 1,167 participants in the final statistical model, of which 267 (22.9%) died. Mean age
at injury was 31.1 years (standard deviation=13.5), with an average of 9.6 (standard
deviation =6.8) years since injury. 21.5% of participants were ambulatory. The majority
were white (76.9%) and male (74.4%).
Modeling
In the base logistic regression model including only demographic and injury characteristics,
chronologic age, years since injury, and injury severity were significant (table 1). The
second stage added health conditions to the base model. Each of the 5 health factors weresignificant in stage 2 (table 2), although years since injury was no longer significant.
Compared with the reference group (ambulatory), there were greater odds of mortality witheach successive increase in injury severity. Additionally, odds of mortality increased withincreasing age and years since injury.

NIH-PA Author Manuscript

Of the health factors, those who had chronic PUs had 4.52 greater odds of mortality (CI =2.57, 7.94) and those with non-chronic PUs had a 1.46 greater odds of mortality (CI = 1.08,
1.97). Amputation was associated with a 2.69 greater odds of mortality (CI = 1.44, 5.02).
Having been hospitalized at least once in the year prior to the data collection was associatedwith a 1.63 greater odds of mortality (CI = 1.23, 2.14). A diagnosis of PMD was associatedwith a greater odds of mortality (OR = 1.60; CI = 1.21, 2.11). Symptoms of infections was
also related to mortality, with an increase of 1 standard deviation of infectious symptoms
associated with 1.18 greater odds (CI = 1.03–1.36).
Life Expectancy

NIH-PA Author Manuscript

LE calculations were made for each health condition (table 3) using the results from the final
model (table 2). We used a male, injured at 19, with a non-cervical, non-ambulatory injury
as the example. Three different age examples are provided: 20, 40, and 60 years. Underfavorable conditions (no health risk factors), LE was 45.6 years at 20, an additional 28.7
years at 40, and 14.2 years at 60. The projected lost LE associated with each condition
increased with age. The greatest average decline in LE was associated with chronic PUs(50.3%), with substantially less reduction for non-chronic (14.6%). Of the dichotomous
variables, the greatest percentage of lost LE was for amputation (35.4%), followed byhospitalization (18.5%), then PMD (18%). LE decreased an average of 6.7% for eachstandard deviation on the symptoms of infections scale. Although the percentage of lost LE
is high for some conditions, 29.8% of participants reported no risk factors and 43.4%
reported only one risk factor (table 4). Chronic PUs and amputation, associated with thegreatest lost LE, were only reported by 2.7% and 2.1% of the participants, respectively.

Discussion
The results underscore the profound effects of secondary health conditions on LE after SCI.
Whereas previous research had indicated only a modest effect of health on LE,4,13 thecurrent study suggests substantial lost LE related to several factors. The differences between
studies are likely attributable, at least in part, to the data sets utilized. Previous studies werelargely based on analysis of NSCISC data, which is limited to a more circumspect numberof predictors.
The presence of even a single secondary condition should be considered a threat to LE, withchronic PUs and amputations being particularly problematic. Other secondary healthconditions, including infections and clinical depression, are also of concern. The presence of
all cause hospitalization was also related to diminished LE, even though the intervalArch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

Krause and Saunders

Page 6

NIH-PA Author Manuscript

between data collection and determination of mortality was 10 years (, i.e., hospitalization in
the 12 months prior to data collection predicted mortality 10 years later). The pattern ofpredictors suggests there is likely an overall pattern of diminished health manifested throughseveral secondary conditions, ultimately leading to a heightened risk of mortality anddiminished LE. These findings are generally consistent with the existing literature in termsof risk of mortality. There has been insufficient literature on LE from which to draw
comparisons.
Although declines in LE associated with secondary conditions were profound, not all
participants reported risk factors (29.8% reported no conditions), and another 43.4%
reported only 1 condition. Therefore, risk of diminished LE was focused on a subset ofparticipants. Furthermore, the frequency of occurrence of the most important risk factors
was rare, as chronic PUs and amputations were reported by less than 3% of the sample.
Although non-chronic PUs were more common (54%), the amount of lost LE wassubstantially less (14.6%) when compared with chronic PU (50.3%).

NIH-PA Author Manuscript

Several important methodologic considerations are in this study. First, the health risk factors
not only vary in terms of their prevalence and association with LE but also in their nature.
For instance, amputations are permanent and likely relate to additional chronic conditions,such as diabetes. Although not truly permanent, chronic PUs was defined by essentiallyhaving PUs all the time. This is different than how other variables were measured, such asscores consistent with a diagnosis of depression at the time of the study or hospitalization for
symptoms of infection within the previous 12 months. Therefore, the nature of themeasurement of the variable itself may affect the LE, with the more permanent or chronic
measures associated with more diminished LE. Had we been able to reliably measure
depression, hospitalization, and symptoms of infection over time, it is almost certain thosewith chronic levels of these conditions would report greater diminished LE.
Clinical Implications

NIH-PA Author Manuscript

There are many ways the current findings can be translated into clinical practices. The
identification of the predictorswill allow clinicians from multiple disciplines to assess risk
for mortality quickly and efficiently. A minimum intervention for any clinician is to share
information with the individual who has SCI. Clinicians may utilize the information on thespecific risk factors to develop interventions in their own area of expertise. For instance,
rehabilitation psychologists should ensure assessments for depression are routine upon
outpatient visits and those at high risk for depression are identified and appropriate followup is implemented. Reviewing history of infections upon outpatient visits is also important.
Other types of assessment, such as skin integrity, are almost certainly more routine.
Interventions need to be developed targeting stakeholders more directly by disseminating
information to them which may be utilized to promote self-health. Those with SCI certainly
know chronic PUs lead to declining health, but knowing the extent to which they result in
diminished LE may be more likely to lead to changes in health behaviors.
Study Limitations
There are several study limitations. First, the participant cohort was drawn after the onset of
SCI, so substantial mortality could have occurred prior to enrollment. This may affect theabsolute LE but would not likely affect the strength of a predictor factor. If so, it would
probably diminish the apparent significance of a predictor. Second, there was a 10-year
interval between prospective data collection and determination of mortality status, such that
health factors may have changed substantially over time. This also may result inunderestimating their relationships with mortality, particularly for variables not measured
over time (i.e. depression) or only over a relatively short period of time (i.e. hospitalizations,Arch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

Krause and Saunders

Page 7

NIH-PA Author Manuscript

infections). Third, all data were self-report. Although we do not anticipate this as highly
problematic given the basic nature of their reporting of variables, any discrepancy of selfreport with real status of the variables would result in an underestimate of the true
relationships with mortality, and data from self-report and medical records may produce
different results.25 Fourth, we utilized proxy variables for classification of injury severity(use of ambulatory status rather than ASIA grades), and this could have resulted in somedifferences in absolute LE (i.e., the true relationship of injury severity with mortality may
have been underestimated). Fifth, although the NDI is highly accurate, failure to identify any
deaths would have artificially inflated LE estimates. However, this would not necessarily
result in different patterns of relationships between risk factors and mortality. Sixth, life
expectancy estimates are averages. True life expectancy may vary substantially between
individuals based on additional characteristics not accounted for in this study. Seventh, thestudy results are based on data from only one specialty hospital in the Southeastern UnitedStates. Lastly, the sample size was large compared with the majority of studies, but still
smaller than those using NSCISC data. “Although the initial response rate was high (72%),
attrition and enrollment could be selective (e.g., people in poor health less likely to respond).
Future Research

NIH-PA Author Manuscript

Ongoing research is needed to incorporate more frequent assessments of risk and protective
factors and incorporate additional parameters, including biomarkers of stress, age, andvascular health. These assessments should take place over intervals longer than 10 years.
Quality of life indicators also need to be incorporated into the model, as they were among
the first factors to be identified in association with mortality.26–28 We need to identify theunderlying mechanisms for the observed findings, as we do not know whether these factors
were significant by virtue of being indicators of overall health, or whether the physiologic
processes related to amputations actually contribute to premature mortality. Lastly,
investigation of an expanded set of risk and protective factors in association with causes ofdeath would dramatically enhance our understanding of premature mortality and guideintervention strategies to promote greater longevity.
Conclusions
Several secondary health conditions represent risk factors for mortality and diminished LE
after SCI. Health care providers should take the presence of 1 or more of these factors as anindicator of the need for intervention. Early intervention could potentially lengthen LE.

Acknowledgments
NIH-PA Author Manuscript

Sponsorship: The contents of this publication were developed under grants from the Department of Education,
NIDRR grant numbers H133G050165, H133N50022, and H133B090005, and the National Institutes of Health,grant number 1R01 NS 48117. However, those contents do not necessarily represent the policy of the Departmentof Education or NIH, and you should not assume endorsement by the Federal Government.
We certify that no party having a direct interest in the results of the research supporting this article has or will
confer a benefit on me or on any organization with which we are associated AND, if applicable, we certify that all
financial and material support for this research (eg, NIH or NHS grants) and work are clearly identified in the title
page of the manuscript.

List of Abbreviations
ASIA

American Spinal Injury Association

CHART

Craig Handicap Assessment Reporting TechniqueCI

confidence intervalArch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

Krause and Saunders

Page 8

NIH-PA Author ManuscriptLElife expectancy

NDI

National Death Index

NSCISC

National Spinal Cord Injury Statistical Center

OAHMQ

Older Adult Health and Mood QuestionnaireORodds ratio

PMD

probable major depression

PU

pressure ulcer

SCI

spinal cord injury

References

NIH-PA Author Manuscript
NIH-PA Author Manuscript

1. NSCISC. Annual Statistical Report. Birmingham, AL: University of Alabama; Nov. 2009
2. DeVivo MJ, Krause JS, Lammertse DP. Recent trends in mortality and causes of death among
persons with spinal cord injury. Arch Phys Med Rehabil Nov. 1999; 80(11):1411–1419.
3. Strauss DJ, DeVivo MJ, Paculdo DR, Shavelle RM. Trends in life expectancy after spinal cord
injury. Arch Phys Med Rehabil. 2006; 87:1079–1085. [PubMed: 16876553]
4. Krause JS, DeVivo MJ, Jackson AB. Health status, community integration, and economic risk
factors for mortality after spinal cord injury. Arch Phys Med Rehabil. 2004; 85:1764–1773.
[PubMed: 15520971]
5. Krause JS. Secondary conditions and spinal cord injury: A model for prediction and prevention. Top
Spinal Cord Inj Rehabil. 1996; 2(2):217–227.
6. Krause JS, Carter RE, Pickelsimer E, Wilson D. A prospective study of health and risk of mortality
after spinal cord injury. Arch Phys Med Rehabil. 2008; 89:1482–1491. [PubMed: 18674984]
7. Krause JS, Carter RE, Pickelsimer E. Behavioral risk factors of mortality after spinal cord injury.
Arch Phys Med Rehabil. 2009; 90(1):95–101. [PubMed: 19154835]
8. Krause JS, Carter R, Zhai Y, Reed K. Psychologic factors and risk of mortality after spinal cord
injury. Arch Phys Med Rehabil. 2009; 90(4):628–633. [PubMed: 19345779]
9. Krause JS, Carter RE. Risk of mortality after spinal cord injury: relationship with social support,
education, and income. Spinal Cord. 2009; 47(8):592–596. [PubMed: 19255586]
10. Lidal I, Snekkevik H, Aamodt G, Hjeltnes N, Biering-Sorensen F, Stanghelle J. Mortality after
spinal cord injury in Norway. J Rehabil Med. 2007; 39(2):145–151. [PubMed: 17351697]
11. Garshick E, Kelley A, Cohen SA, et al. A prospective assessment of mortality in chronic spinal
cord injury. Spinal Cord. 2005; 43(7):408–416. [PubMed: 15711609]
12. Krause JS, Zhai Y, Saunders LL, Carter RE. Risk of mortality after spinal cord injury: an 8-year
prospective study. Arch Phys Med Rehabil. 2009; 90(10):1708–1715. [PubMed: 19801060]
13. Strauss D, DeVivo M, Shavelle R, Brooks J, Paculdo D. Economic factors and longevity in spinal
cord injury: a reappraisal. Arch Phys Med Rehabil. 2008; 89(3):572–574. [PubMed: 18295640]
14. Krause JS, Saunders LL, DeVivo M. Income and risk of mortality after spinal cord injury. Arch
Phys Med Rehabil. in press
15. Krause JS, Saunders LL. Life expectancy estimates in the life care plan: Accounting for economic
factors. J Life Care Plan. 2010; 9(2):15–28.
16. Krause JS, Saunders LL. Risk of mortality and life expectancy after spinal cord injury: The role ofhealth behaviors and participation. Top Spinal Cord Inj Rehabil. 2010; 16(2):53–60. [PubMed:
21960734]
17. [Accessed September 28, 2007.] National Death Index. http://www.cdc.gov/nchs/ndi.htm
18. Kemp BJ, Adams BM. The Older Adult Health and Mood Questionnaire: A measure of geriatricdepressive disorder. J Ger Psychiat Neuro. 1995; 8(3):162–167.
19. Craig A, Tran Y, Middleton J. Psychological morbidity and spinal cord injury: a systematic review.
Spinal Cord. 2009; 47(2):108–114. [PubMed: 18779835]
Arch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

Krause and Saunders

Page 9

NIH-PA Author Manuscript

20. Krause JS, Saunders LL, Reed KS, Coker J, Zhai Y, Johnson E. Comparison of the Patient Health
Questionnaire and the Older Adult Health and Mood Questionnaire for self-reported depressive
symptoms after spinal cord injury. Rehabil Psychol. 2009; 54:440–448. [PubMed: 19929126]
21. Strauss D, Shavelle R, DeVivo MJ, Day S. An analytic method for longitudinal mortality studies. J
Ins Med. 2000; 32:217–225.
22. Shavelle RM, DeVivo MJ, Strauss DJ, Paculdo DR, Lammertse DP, Day SM. Long-term survivalof persons ventilator dependent after spinal cord injury. J Spinal Cord Med. 2006; 29(5):511–519.
[PubMed: 17274490]
23. Hosmer, DW.; Lemeshow, S. Applied logistic regression. 2. New York, New York: John Wileyand Sons; 2000.
24. DeVivo MJ. Estimating life expectancy for use in determining lifetime costs of care. Top Spinal
Cord Inj Rehabil. 2002; 7(4):49–58.
25. Jones MP, Bartrop R, Dickson HG, Forcier L. Concordance between sources of morbidity reports:
Self-reports and medical records. Frontiers in Pharmacology. 2011:2. [PubMed: 21687499]
26. Krause JS, Crewe NM. Prediction of long-term survival of persons with spinal cord injury: An 11
year prospective study. Rehabil Psychol. 1987; 32:205–213.
27. Krause JS, Saari JM, Dykstra D. Quality of life and survival after spinal cord injury. SCI
Psychosocial Process. 1990; 3:4–8.
28. Krause JS. Survival following spinal cord injury: A fifteen-year prospective study. Rehabil PsycholSum. 1991; 36(2):89–98.

NIH-PA Author Manuscript
NIH-PA Author Manuscript
Arch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

Krause and Saunders

Page 10

NIH-PA Author Manuscript

Figure 1.

Flowchart of participant inclusion in the final analysis.

NIH-PA Author Manuscript
NIH-PA Author Manuscript
Arch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

Krause and Saunders

Page 11

Table 1

NIH-PA Author Manuscript

Odds ratios, 95% confidence intervals, and mortality rates as a function of demographic and injury
characteristics.
Mortality
Demographic/Injury Characteristics

Yes (n=267)

No (N=900)
OR* (95% CI)

Row %p-value

GenderMale (n=868)

23.7

76.3

1.17 (0.88–1.68)

Female (n=299)

20.4

79.6

Reference

36.7

63.3

7.33 (4.14–12.95)

C5–C8 (n=356)

25.3

74.7

3.77 (2.27–6.26)

Non-cervical (n=402)

20.9

79.1

2.58 (1.58–4.24)

0.3841
Injury Severity
C1–C4 (n=158)

Ambulatory (n=251)<.0001

13.9

86.1

ReferenceYears Since Injury

10.4 (7.3)

9.3 (6.6)

1.07 (1.05–1.10)<.0001

Age at Survey

49.7 (15.3)

38.0 (11.4)

1.08 (1.06–1.10)<.0001

NIH-PA Author Manuscript*

OR are controlling for all other variables in the table.

NIH-PA Author Manuscript
Arch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

Krause and Saunders

Page 12

Table 2Adjusted odds ratios and 95% confidence intervals for the risk variables from the final model.

NIH-PA Author Manuscript

Final ModelOR*

Variable

95% CI

0.1442

Gender (vs. Female)Malep-value

1.26

0.96–1.83

35–39

1.74

0.93–3.26

40–44

2.80

1.57–5.01

45–49

2.47

1.35–4.54

50–54

3.77

2.11–6.73

55–59

4.24

2.32–7.75

60–64

6.48

3.46–12.12

65–69

9.60

4.93–18.69

70–74

19.02

10.05–35.99

75–79

28.80

14.37–57.72

80+

41.94

19.32–91.04<.0001Age (vs. 18–34)

NIH-PA Author Manuscript<.0001

Injury Severity (vs. Ambulatory)
C1–C4

3.06

1.90–4.92

C5–C8

1.86

1.19–2.91

Non-cervical

1.53

0.98–2.39
0.6299Years Since Injury (vs. 10–19)
1–4

1.68

0.98–2.39

5–9

0.95

0.65–1.37

20–29

1.11

0.81–1.51

30+

1.01

0.60–1.70

Non-chronic

1.46

1.08–1.97

Chronic

4.52

2.57–7.94<.0001Pressure Ulcers (vs. Never get them)

0.0009Probable Major Depression (vs. No)
Yes

1.60

1.21–2.11

NIH-PA Author Manuscript

0.0006

Hospitalizations (vs. None)
1+

1.63

1.23–2.14
0.0018

Amputation (vs. No)
Yes

2.69

1.44–5.02

Infections**

1.18

1.03–1.36

0.0159*controlling for age, gender, years post-injury, injury severity**

OR reported is for 1 sd change in infectious symptoms

Arch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

NIH-PA Author Manuscript

NIH-PA Author Manuscript
13.2 %
13.6 %
27.6 %
4.8 %

39.58
39.37
33.02
43.42

1+ Hospitalizations

Amputation

Infections (>1 s.d.)

26.87

18.83

23.62

23.78

14.40

24.72

28.67

LE-40YO

Percentage of lost life expectancy due to the presence of the condition.

*

NOTE: example is for a male, with non-cervical, non-ambulatory SCI

41.5 %

26.65

Chronic

10.6 %

% LE
LOST*

40.76

45.59

LE-20YO

Probable Major Depression

Non-chronic

Pressure Ulcers

No health conditions

Health Condition

6.3 %34.3 %

17.6 %

17.1 %

49.8 %

13.8 %

% LE
LOST*

12.90

7.90

10.72

10.82

5.72

11.43

14.17

LE-60YO9.0 %44.3 %

24.4 %

23.6 %

59.6 %

19.3 %

% LE
LOST*

6.7%

35.4%

18.5%

18.0%

50.3%

14.6%

Avg LE
LOST

Life expectancies as a function of health status for a hypothetical case at ages 20, 40, and 60.

NIH-PA Author Manuscript

Table 3Krause and Saunders
Page 13

Arch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.

Krause and Saunders

Page 14

Table 4

Percentage of participants affected by specific health factors.

NIH-PA Author Manuscript

Behaviors

Percentage

No conditions

29.82

Pressure Ulcers
Non-chronic

53.98

Chronic

2.66

Probable Major Depression

23.48

1+ Hospitalizations

6.86

Amputation

2.14

Infections Symptoms (>1 s.d.)

18.34

Multiple Conditions

NIH-PA Author Manuscript

1 condition only

43.44

2 conditions

18.34

3 conditions

6.34

4 conditions

1.97

5 conditions

0.09

*

sum of the # of conditions.

NIH-PA Author Manuscript
Arch Phys Med Rehabil. Author manuscript; available in PMC 2012 November 01.