(Stroke. 2000;31:2603.)
© 2000 American Heart Association, Inc.
Original Contributions |
Depression and Other Determinants of Values Placed on Current Health State by Stroke Patients
Evidence From the VA Acute Stroke (VASt) Study
Presented as preliminary results at the 17th Annual Health Service Research and Development Meeting, Washington, DC, February 24–26, 1999.
From Health Services Research and Development (H.B.B., R.D.H., L.J.E., D.B.M.) and Epidemiologic Research and Information Center (R.D.H.), Durham Veterans Affairs Medical Center, and the Center for Aging and Human Development (H.B.B., R.D.H.), the Department of Medicine, Division of General Internal Medicine (H.B.B., R.D.H., D.B.M.), the Department of Psychiatry and Behavioral Sciences (H.B.B.), the Department of Family and Community Medicine, Division of Biometry (L.J.E.), and the Center for Clinical Health Policy (D.B.M.), Duke University Medical Center, Durham, NC.
Correspondence to Hayden B. Bosworth, PhD, Health Services Research and Development, Building 16, Room 70, Durham Veterans Affairs Medical Center (152), 508 Fulton St, Durham NC 27705. E-mail hboswort{at}acpub.duke.edu
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Abstract |
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Background and Purpose—This prospective study examined the determinants of the utility (value) placed on health status among a sample of patients with acute ischemic and intracerebral hemorrhagic stroke.
Methods—Data were from the VA Acute Stroke (VASt) study, a
nationwide prospective cohort of 1073 acute stroke patients admitted at
any of 9 Department of Veterans Affairs Medical Center sites between
April 1, 1995, and March 31, 1997. The primary outcome was the
patient’s health status utility as measured by the time-tradeoff
method. Data were obtained by telephone interviews at 1, 6, and 12
months and by medical record review. General linear mixed modeling
was used to assess the effects of social, psychological, and physical
factors on patients’ valuations of their current health state. The
analysis was confined to the 327 patients who were able to
provide self-reports at 
2 time points.
Results—Patients’ valuations of their health state status over the initial 12 months after stroke were very stable over time, with only a slight improvement at 6 months, followed by a slight decline at 12 months. In adjusted analyses, living alone, being institutionalized, decreased physical function, and depression were independently associated with lower levels of patient health status utility over time.
Conclusions—Stroke patient health status utilities are relatively stable during the initial year after stroke. In addition to physical function, psychological health and social environment are important determinants of patient health status utility. These factors need to be considered when conducting stroke decision analyses if more accurate conclusions are to be drawn regarding preferred patterns of care.
Key Words: depression • physical function • quality of life • stroke outcome
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionAppendix 1References |
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The utility (value) placed by a patient on his or her current physical health state is a key concept in health outcome assessment of stroke, especially for disease policy modeling. Decision theory indicates that when utility for relevant health states is assessed in terms of willingness to make tradeoffs to avoid those states, then the predicted average utility resulting from a particular intervention is a meaningful guide to the overall value of that intervention.1 However, little is known empirically about the determinants of health status utility and, hence, its meaning and generalizability. To date, health status utility scores for various stroke practices have been derived from patients at risk for future major stroke (eg, see References 2 5 ) and from patients who have never experienced stroke (eg, see References 3 6 ). As might be anticipated, such patients respond with low health status utilities for severe physical impairments. Yet, it is clear that patients who actually experience a high level of impairment give a high utility score to their current health state (eg, see Reference 7 ).
A number of factors beyond the actual level of physical functioning may modify the true utility score that patients have for their state of health. One potentially important factor is time. It may be that as the stroke patient recovers or adapts to a given level of physical function, the associated health status utility score may increase. If health status utility varies with time, the point at which the utility is solicited could greatly affect the "optimal" treatment strategy identified.
Other potentially modifying factors include psychological health status and the individual’s living environment, particularly the availability of social support. Health status utility may be especially sensitive to depression, decreasing with the onset of depression. Depression is common among survivors of stroke; it affects up to one third of patients even as late as 3 years after the event.8 9 10 Health status utility may also change with the amount of social support received or perceived, either for the short or longer term.11 12 13
Using data from a 9-site prospective cohort study of stroke patients who were clinically managed at Department of Veterans Affairs (VA) Medical Centers, we were able to assess the relative importance of psychological health (and social environment) vis-à-vis physical function as independent determinants of patients’ evaluations of their current health states. We were also able to explore the stability of stroke patient health status utilities over the initial year after stroke.
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Subjects and Methods |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionAppendix 1References |
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Study Design
The present study is a secondary analysis that uses data from the VA Acute Stroke (VASt) study, which involved a prospective cohort of 1073 patients who were hospitalized for acute stroke at any of 9 VA hospitals between April 1995 and March 1997.14 The VASt study followed patients from presentation through 1 year after stroke onset. Data were obtained on key acute care diagnostic and therapeutic procedures that were used and on a broad array of physical and psychological health outcomes. Data on in-hospital care came from medical record review, and outcome data came from telephone interviews that were conducted at 1, 6, and 12 months after admission. The institutional review board at each participating site approved the VASt study protocols.
Patient Population
The patients represent a virtually complete census of
patients with acute ischemic and intracerebral
hemorrhagic stroke seen at these sites during the study period.
Patients were eligible for the present study if they had a
confirmed diagnosis of intracerebral hemorrhage
(International Classification of Diseases, 9th Revision, Clinical
Modification [ICD-9-CM] 431) or cerebral infarct (ICD-9-CM 434 and
436). Patients were excluded if the stroke was iatrogenic, secondary to
either brain tumor or trauma, occurred during hospitalization for
another condition, was an extension of a previous stroke, or had an
onset >7 days before the admission. Patients with a previous stroke
were included in the cohort unless the stroke leading to the admission
was an extension of the initial stroke.
Data Collection
A research assistant at each site identified potentially
eligible patients within 48 hours of admission by reviewing the
hospital’s admission logbook for patients admitted with symptoms
suggestive of stroke. The diagnosis was initially confirmed by review
of the medical record and, when necessary, discussion with the
attending physician. To ensure that no stroke patient was missed, the
computerized discharge files of each hospital were screened for
patients discharged with a diagnosis of intracerebral
hemorrhage or acute cerebral infarct. Approximately 11% of the
patients were identified in this manner. A final review of the medical
record was conducted to confirm that all eligibility criteria were
met. Eligible patients were enrolled as soon after identification as
possible; for the majority of patients, this occurred in the hospital,
but for some, it was at the first interview.
All telephone interviews were performed from a central site by one
interviewer who was experienced in interviewing severely ill patients.
Follow-up data were available for 881 (82%) of the 1073 patients. For
the present study, because of our emphasis on psychological health
outcomes, we restricted the cohort to those patients who did not
require a proxy respondent (ie, those were able to understand and
communicate with the interviewer or were not cognitively impaired, as
determined by the Short Portable Mental Status
Questionnaire,15 a cognitive screening test). Proxy
information was not included in the present study because proxies
were not considered capable of providing valid responses to such
psychological health measures as preference for current health state or
depressive symptoms. Compared with those patients who did not require
proxy informants, patients who required proxy informants had lower
levels of physical function as measured by the Barthel Index and were
more likely to have been institutionalized . We further restricted the
cohort to those patients who participated in at least 2 of the 3
follow-up interviews because we were interested in examining changes in
patients’ current health perceptions. Patients who had 2 follow-up
interviews did not differ significantly from those patients who had a
single interview in terms of demographic factors, stroke severity, or
clinical care received (data available from the authors on request).
Our final sample was composed of 327 patients.
Outcome
Our outcome was patient health status utility, which was
measured by the time-tradeoff method.16 This generic
measure of utility is based on the patient’s willingness to trade
hypothetical years of life in the current health state to live in
excellent health. We used the standard approach that involved a 10-year
base for current health state with the trade being 1-year decrements
(with subdecrements of 6 and 3 months) in excellent health until the
patient reached a point where he or she was indifferent to the trade.
The final utility score can range from 0.25 to 9.75, with lower scores
indicating lower valuation of current health state, ie, a willingness
to trade more years of life to avoid the current health
state.16
Primary Explanatory Variables
The factors of primary interest included time, social
environment, physical function and, especially, depressive
symptomatology. Because of the nature of the cohort, to minimize
patient burden, an abbreviated version of the depression scale of the
Center for Epidemiologic Studies (CES-D scale) was used to solicit
symptoms of clinical depression.17 The abbreviated CES-D
scale is based on the full CES-D scale,18 which is a
20-item self-report scale designed to measure depressive symptomatology
in a general population. The questions refer to symptoms experienced
during the week before the interview. We classified patients as
positive for depressive disorder (1 indicating yes; 0, no) if they
scored 0.06 on the scale.17 Analyses of data
from a general population, primary care patients, and mental health
patients (n=3000) showed that the measure with a cutoff of 0.06 had a
high sensitivity (89%) and positive predictive value for detecting
depressive disorder (specificity 95%) in the past month, especially
for those that met full criteria for depression as assessed by
Diagnostic and Statistical Manual of Mental
Disorders, edition 3.18
Physical function was assessed by the patient’s ability to perform activities of daily living as measured by the Barthel Index.19 This 10-item scale has a self-care component that includes questions on eating, grooming, and toilet capabilities and a mobility component with items involving ability to transfer and ambulation. Scores range from 0 to 100, with higher scores signifying better functioning. Interrater reliability is high (r=0.99) when used with stroke patients.20
Social environment was measured by living situation and marital status. Social environment is known to influence recovery from stroke.21 It was divided into 3 categories: living with someone, living alone, or being institutionalized at the time of discharge. For analytic purposes, living with someone was used as the referent category.
Covariates
Covariates were selected on the basis of a theoretical or
empirically documented association with patient health status
utility.
Demographic information included age, race, sex, and education.
Stroke severity, a major determinant of residual physical and, perhaps,
psychological impairment, was measured by the modified Canadian
Neurological Scale.22 The original scale was modified for
retrospective ascertainment of severity by use of medical record
data. The modified scale is a valid and reliable instrument; for this
data set, the intrarater and interrater reliability were high, with a
weighted 
value of 0.77 and 0.79, respectively.14
Scores range from 0 to 11.5, with lower scores indicating greater
severity.
Stroke type was classified as either hemorrhagic stroke (ICD-9-CM 431) or ischemic stroke (ICD-9-CM 434, 436). There is evidence indicating that stroke type may be related to "vascular depression"23 24 and, subsequently, may influence patient health status utility.
Level of care was assessed by considering whether neurologists or other specialists, particularly internists, managed the stroke patient. Current evidence suggests that neurologists select patients who have a better prognosis on the basis of clinical characteristics.25 26 Thus, this variable "captures" those clinical characteristics that influence health outcomes and, therefore, are likely to be associated with health status utility measurements.
Data Analysis
Because of our primary interest in the role of depression, the
analysis was oriented toward understanding the modifying effect
of depression on patient health status utility. We first examined
whether individuals identified as being depressed differed from
nondepressed patients at baseline in terms of key characteristics. To
assess differences, we used the 
2 statistic
for categorical variables and ANOVA for continuous variables.
The stability of the patient health status utility over time and the
relationship between patient health status utility, depression, and
physical function were assessed by Pearson correlation
coefficients.
To determine the independent effect of depression on patient health status utility, we used a general linear mixed-effects model27 28 to estimate the association between patient utility and depression, adjusting for social environment, physical function, and covariates over the 1-year period. The mixed model is able to explicitly adjust for missing values (eg, death, loss to follow-up, and refusal to be interviewed), repeated measurements, and time dependency of variables. Thus, we were able to determine associations and tests of hypotheses about population parameters (fixed effects) while simultaneously determining associations and tests of hypotheses about patient-specific parameters (random effects).
For the initial specification of the model, we included the following:
linear time and quadratic time; the demographic characteristics of age
(continuous), race (African American versus white), and education (<12
years, 12 years, and >13 years of education); level of care
(neurologist versus other specialist); severity of stroke (continuous);
stroke type (ischemic versus hemorrhagic); social environment,
as indicated by living situation (living with someone, living alone, or
institutionalized) and marital status (married versus otherwise);
physical function via the Barthel index (continuous); and the presence
of depression (depressed versus not depressed). Depression was
considered a static variable, indicating patient depression at 1
follow-up contacts.
Because of the number of variables, the initial mixed model used only main effects (ie, no interactions). To obtain the most parsimonious model, our model reduction procedure involved a backward selection, removing one variable at a time on the basis of the size of the P value. As a covariate was removed, the model was refitted with the remaining variables before removing another variable. Selected interactions were investigated after the final reduced model had been identified; however, no significant interactions were observed. The slope and intercept were modeled as random effects. The final model contained 946 observations from the 327 patients.
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Results |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionAppendix 1References |
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Sample Characteristics
Approximately 16% of the patients (n=51) were identified as depressed at baseline, and almost 30% of the sample screened positively for depression at some time during the 1-year follow-up period. The characteristics of patients who were and were not depressed at baseline are displayed in Table 1
. Compared with the nondepressed stroke
patients, the depressed patients were younger (P<0.02),
more likely to be white (P<0.04), and were willing to trade
more years of life to live in excellent health as indicated by the
health status utility measure (P<0.0001). Patients who were
depressed at baseline did not differ from nondepressed patients on
level of education, marital status, living situation, stroke severity,
or physical function.
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Stability of Patient Health Status Utility Over Time
Patient health status utility measurements were stable over 12
months. The correlation between valuations at 1 and 6 months was 0.88
(P<0.0001), and the correlation between valuations at 1 and
12 months was 0.87 (P<0.0001).
Correlations Between Depression, Physical Function, and Patient
Health Status Utility
Depression was inversely associated with health status utility at
1 month (r=-0.23, P<0.0006), 6 months
(r=- 0.26, P<0.0001), and 12 months
(r=-0.27, P<0.0001) after stroke. Physical
function was not significantly correlated with patient health status
utility at either 1 month (r=0.10, P<0.27) or 12
months after stroke (r=0.07, P<0.31) but was
correlated at the 6-month assessment, although the correlation was
modest (r=0.15, P<0.025). Physical function was
significantly correlated with depression at 6 months
(r=-0.24, P<0.001) but not at either 1 month
(r=-0.08, P<0.24) or 12 months
(r0.02, P<0.75) after stroke.
Adjusted Association Between Depression and Patient Health
Status Utility
On the basis of the mixed-effects model, linear time, quadratic
time, living alone, being institutionalized, physical function, and
depression were significantly related to patient health status utility
(Table 2). The significant linear
(P<0.006) and quadratic (P<0.03) time effects
indicated that patient health status utilities significantly increased
6 months after baseline and then decreased by 12 months after stroke.
Patients who were depressed reported worse valuations of their health
states over time than did other patients. Other factors that were
related to lower health status utility over 12 months after stroke
included living situation (living alone versus living with someone,
P<0.03; being institutionalized versus living with someone,
P<0.03) and worse physical function (P<0.0001).
Race, marital status, education, stroke severity, level of care, and
type of stroke were unrelated to valuation of current health state
among stroke patients over time.
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Discussion |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionAppendix 1References |
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This is one of the first studies to examine psychological health and other determinants of patient health status utility scores among actual stroke patients and the stability of the scores over time. Virtually all previous studies on health status utility measurement have been based on hypothetical assessments and without regard for time of the assessment (relative to stroke onset) or psychological health state. Such studies imply that stroke-related physical impairments are the key determinants of a stroke patient’s valuation of his/her current health state.
We found that psychological health was an independent predictor of health status utility and at least as important as physical functioning. Even after adjusting for physical functioning and other factors that may influence how a stroke patient values his health state, patients who had significant depressive symptoms reported lower health status utility for their current health state, and this lower valuation persisted over time. Moreover, social environment, ie, living with someone, was another important determinant of health status utility. These results suggest that both physical and psychological health need to be considered in decision modeling of stroke practice patterns and outcomes. An additional reason for understanding health status utility is that it is related to patient compliance.5 That is, patients who report low levels of health status utility scores are more likely not to comply or respond to treatment and rehabilitation as well as those who have higher levels of health status utility scores.
Decision modeling relies on health status utility scores in selecting preferred practices. Utility scores provide explicit information on the quality of life, which also incorporates the patients’ values. In soliciting utility assessments from patients for certain outcomes associated with practices, it is important to know the array of factors that will determine the assigned utility score. The present study shows that more than physical function must be incorporated into the process; utility scores must be adjusted for psychological health and social environment as well as for physical function.
This is the first study to show that stroke patients’ preferences for current health state are stable over time, at least during the first year after stroke occurrence. Although the time trend was statistically significant, the changes were small and unlikely to substantially affect conclusions drawn under decision-modeling exercises. This suggests that at least during the first year after stroke, the time point at which health status utilities are assessed may be less important than the patient’s psychological health status and social environment.
Our findings on the importance of depression in determining health status utility have particular relevance for poststroke rehabilitation. Given innovations in the acute management of stroke and the accompanying decrease in the case fatality rate, an increasing number of stroke victims are surviving with residual disability. Little attention has been paid to the effects of depression on rehabilitative outcomes29 despite major depression being highly prevalent and persistent over time among stroke patients.30 31 32 33 34
A better understanding of the poststroke effects of depression is particularly important in light of the increasing evidence indicating that depression is a significant predictor of mortality,35 36 37 rehospitalization,38 39 and increased disability.40 Morris et al41 found that patients who were rated as depressed 2 weeks after stroke were 3.4 times more likely to die over the subsequent 10 years. In addition, depression in poststroke patients has been shown to limit the degree of recovery from stroke at both 6 months42 and 2 years.43 Parikh et al43 concluded that early detection and treatment of depression might lessen the negative effects that depression can have on recovery.
Although we found that physical function and depressive symptoms are not highly correlated, previous studies of the association between depression and physical function in stroke patients have yielded inconsistent findings. Stern and Bachman44 found no relationship between depression and the ability to perform the activities of daily living. Others report a significant, but weak, correlation between these 2 variables.45 46 It seems likely that depressive symptoms are more related to some physiological change than solely to a response to loss of physical function. Although we did not examine any neurobiological factors, the importance of lesion location may be more important as a predictor of depression than loss of physical function.47 48 49
Similar to the findings of Astrom et al,10 we found that poor social environment (eg, living alone or being institutionalized) was inversely related to patients’ valuations of their health states. Patients who lack social support, live alone, or have never been married are known to have an elevated risk of mortality50 51 52 53 54 and morbidity (ie, see Reference 55 ). Moreover, as individuals experience highly debilitating diseases such as strokes, there is a potential for a disruption of their social support system. Such disruptions in the social support system may take the form of actual reductions in the social support network or perceived loss of social support, and psychological and biological repercussions are likely (eg, depression).56 Stroke patients with minimal social support networks may be targeted as being at higher risk for subsequent problems.
Although little research has been conducted to assess the relationship between severity of stroke and patient preference, we found no relationship between baseline stroke severity and reported health status utility value. However, to the extent that stroke severity is reflected in more severe deficits, severity may be viewed as being related to health status utility through the level of physical impairment. The present and past studies report an association between health status utility and severity of physical deficits. Although previous studies used hypothetical situations among patients who have not experienced strokes or had only mild strokes (eg, see References 3 6 ), we queried patients who had actually experienced mild to more severe strokes and the associated physical and mental impacts. It appears that patients may be able to accurately assess their likely valuations of various physical health states hypothetically.
We recognize that our findings may be affected by a number of limitations inherent in a study of this type. First, our patient population was restricted to those patients who were able to personally respond to the interview, indicating they were less physically and psychologically compromised by their strokes. Although the differences between responders and nonresponders were not statistically significant, patients requiring proxy respondents had greater physical disabilities, as reflected by lower Barthel Index scores, and a greater proportion were institutionalized than among patients who could provide self-reports. Thus, our conclusions may be relevant only for mild to moderately severe stroke patients, or the relationships between the various determinants and health state preference may be conservative. We could not incorporate information on treatment for depression among patients who screened positively for depression because postdischarge treatment data were not collected. However, the primary care physician of each patient who screened positively for depression was notified of this fact. Therefore, we may have underestimated the prevalence and effects of depression in this sample because it is possible patients may have been receiving antidepressant therapy. Finally, although the VA is an equal access system, the patients are composed of male veterans; hence, the results of the present study need to be verified in a non-VA setting that includes women.
Despite these potential limitations to our findings, we conclude that psychological health and social environment are important determinants of patient health status utility in addition to physical functioning. In conducting stroke policy analyses, investigators need to include factors such as depression and living situation of the patients among the array of covariates if more accurate conclusions are to be drawn regarding preferred patterns of care. Timing of the measurement is a less important concern, because health status utility measurements among a recent stroke patient sample appear to be relatively stable over the 12-month poststroke period.
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Acknowledgments |
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This research was supported in part by the Department of Veterans Affairs, Veterans Health Administration, HSR&D Service, through Program 824 Funds and a Service-Directed Research Project grant (SDR 93-003).
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Footnotes |
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The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.
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Appendix 1 |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionAppendix 1References |
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VASt Study Team
Ronnie D. Horner, PhD (principal investigator), David B. Matchar, MD (co–principal investigator), John R. Feussner, MD (coinvestigator), Beth Armstrong, BA (project manager), Dana Tucker, BA (research assistant), Rick Steeves, BA (research assistant), Barbara Norman, MS (patient interviewer), Michael Jones, BA (third-year medical student assistant), Jennifer Hoff, MS (statistician), and Lloyd Edwards, PhD (senior statistician).
Site Investigators and Research Assistants
David Hess, MD, and Angie Touhey (Augusta, Ga); Sally Zachariah,
MD, and Harilal Bhadja, MD (Bay Pines, Fla); Francis Goldstein, MD,
Martin Durkin, MD, Jodi Calkins, PhD, and John Shelton (Columbia, SC);
Paulette Ginier, MD, and Paula Hensley, RN (Fresno, Calif); Jeffrey
Ferguson, MD, Patricia Flaherty, LPN, and Kim Mihaliak (Indianapolis,
Ind); Sarkis Narzarian, MD, Elizabeth Epperson, and Lee Ann Kennedy,
LPN (Little Rock, Ark); John Taylor, MD, and Nanette Eubank, RN
(Richmond, Va); and Enrique Labadie, MD, Bruce Coull, MD, Priscilla
Somoza, and Lydia Warg-Damiani (Tucson, Ariz).
Project Consultants
Gordon DeFriese, PhD (Center for Health Services Research,
University of North Carolina at Chapel Hill) and Larry Goldstein, MD
(Department of Neurology, Duke University Medical Center, Durham,
NC).
Steering Committee
David Good, MD (Bowman-Gray, Winston-Salem, NC); George Howard,
PhD (Bowman-Gray, Winston-Salem, NC); Pamela Duncan, PhD, PT
(University of Kansas, Kansas City); Byron Hamilton, MD, PhD
(Rehabilitation Research Center, Durham, NC); and Jay Freedman, PhD
(VA Headquarters, Washington, DC).
Received June 27, 2000; revision received August 16, 2000; accepted August 18, 2000.
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References |
|---|
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionAppendix 1References |
|---|
1. von Numann J, Morgenstern O. The Theory of Games and Economic Behavior. Princeton, NJ: Princeton University Press; 1994.
2.
Matchar DB, Pauker SG.
Endarterectomy in carotid disease: a decision
analysis. JAMA. 1987;258:793–798.
3.
Gage BF, Cardinalli AB, Albers GW, Owens DK.
Cost-effectiveness of warfarin and aspirin for prophylaxis of stroke in
patients with nonvalvular atrial fibrillation. JAMA. 1995;274:1839–1845.
4. Gage BF, Cardinalli AB, Owens DK. The effect of stroke and stroke prophylaxis with aspirin and warfarin on quality of life. Arch Intern Med. 1996;156:1839–1836.
5. Samsa GP, Matchar DB, Goldstein L, Bonito A, Duncan PW, Lipscomb J, Enarson C, Witter D, Venus P, Paul JE, Weinberger M. Utilities for major stroke: results from a nationally diverse survey of persons at increased risk for stroke. Am Heart J. 1998;136:703–713.[Medline] [Order article via Infotrieve]
6. Solomon NA, Glick HA, Russo CJ, Lee J, Schulman KA. Patient preferences for stroke outcomes. Stroke. 1994;25:1721–1725.[Abstract]
7. Hallan S, Asberg A, Indredavik B, Wideroe TE. Quality of life after cerebrovascular stroke: a systematic study of patients’ preferences for different functional outcomes. J Intern Med. 1999;246:309–316.[Medline] [Order article via Infotrieve]
8.
King RB. Quality of life after stroke.
Stroke. 1996;27:1467–1472.
9.
Niemi ML, Laaksonen R, Kotila M, Waltimo O. Quality of
life four years after stroke. Stroke. 1988;19:1101–1107.
10.
Astrom M, Adolfsson R, Asplund K. Psychosocial function
and life satisfaction after stroke. Stroke.. 1992;23:527–531.
11. Colantonio A, Kasl SV, Ostfeld AM, Berkman LF. Psychosocial predictors of stroke outcomes in an elderly population. J Gerontol. 1993;48:S261–S268.
12. Evans RL, Bishop DS, Matlock A, Stranahan S, Smith GG, Halar EM. Family interaction and treatment adherence after stroke. Arch Phys Med Rehabil. 1987;68:513–517.[Medline] [Order article via Infotrieve]
13. Osberg JS, DeJong G, Haley SM, Seward ML, McGinnis GE, Germaine J. Predicting long-term outcome among post-rehabilitation stroke patients. Am J Phys Med Rehabil. 1988;67:94–103.[Medline] [Order article via Infotrieve]
14.
Jones MR, Horner RD, Edwards LJ, Hoff J, Armstrong SB,
Smith-Hammond CA, Matchar DB, Oddone EZ. Racial variation initial
stroke severity. Stroke. 2000;31:563–567.
15. Folstein MF, Folstein SE, McHugh PR. "Mini-Mental State": a practical method for grading the cognitive state of patients for the clinical. J Psychiatr Res. 1975;12:189–198.[Medline] [Order article via Infotrieve]
16. Katz JN, Phillips CB, Fossel AH, Liang MH. Stability and responsiveness of utility measures. Med Care. 1994;32:183–188.[Medline] [Order article via Infotrieve]
17. Burnam MA, Wells KB, Leake B, Landsversk J. Development of a brief screening instrument for detecting depression. Med Care.. 1988;26:775–789.[Medline] [Order article via Infotrieve]
18. Radloff LS. The CES-D scale: a self report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385–401.
19. Mahoney FI, Barthel DW. Functional evaluation: the Barthel Index. Md State Med J. 1965;14:61–65.[Medline] [Order article via Infotrieve]
20. Shinar D, Gross CR, Bronstein KS, Licata-Gehr EE, Eden DT, Cabrera AR, Fishman JG, Roth AA, Barwick JA, Kunitz SC. Reliability of the activities of daily living scale and its use in telephone interview. Arch Phys Med Rehabil. 1987;68:723–728.[Medline] [Order article via Infotrieve]
21. Bosworth HB, Hoff HA, Horner RD, Matchar DB, for the VASt Study Team. The importance of the relationship between social networks and quality of life in stroke patients. Am J Epidemiol. 1998;147:S47.
22.
Goldstein LB, Chilukuri V. Retrospective assessment of
initial stroke severity with the Canadian Neurological Scale.
Stroke. 1997;28:1181–1184.
23. Yanai I, Fujikawa T, Horiguchi J, Yamawaki S, Touhouda Y. The 3-year course and outcome of patients with major depression and silent cerebral infarction. J Affect Disord. 1998;47:25–30.[Medline] [Order article via Infotrieve]
24. Krishnan KR, Hays JC, George LK, Blazer DG. Six-month outcomes for MRI-related vascular depression. Depress Anxiety. 1998;8:142–146.[Medline] [Order article via Infotrieve]
25. Horner RD, Matchar DB, Divine G, Feussner JR. Relationship between physician specialty and the selection and outcome of ischemic stroke patients. Health Serv Res. 1995;30:275–287.[Medline] [Order article via Infotrieve]
26.
Mitchell J, Ballard D, Whisnant JP, Ammering CJ, Samsa
GP, Matchar DB. What role do neurologists play in determining the costs
and outcomes of stroke patients? Stroke.. 1996;27:1937–1943.
27. Harville DA. Maximum likelihood approaches to variance component estimation and to related problems. J Am Stat Assoc. 1977;72:320–338.
28. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38:963–974.[Medline] [Order article via Infotrieve]
29. Gordon WA., Hibbard MR. Poststroke depression: an examination of the literature. Arch Phys Med Rehabil.. 1997;78:658–663.[Medline] [Order article via Infotrieve]
30.
Eastwood MR, Rifat SL, Nobbs H, Ruderman J. Mood
disorder following cerebrovascular accidents. Br J
Psychiatry. 1989;154:195–200.
31. Robinson RG. Neuropsychiatric consequences of stroke. Annu Rev Med.. 1997;48:217–229.[Medline] [Order article via Infotrieve]
32. Burvill P, Johnson G, Jamrozik K, Anderson C, Stewart-Wynne E. Risk factors for post-stroke depression. Int J Geriatr Psychiatry.. 1997;12:219–226.[Medline] [Order article via Infotrieve]
33.
Astrom M, Adolfsson R, Asplund K. Major depression in
stroke patients: a 3-year longitudinal study. Stroke. 1993;24:976–982.
34.
House A, Dennis M, Moridge L, Warlow C, Hawton K, Jones
L. Mood disorders in the year after first stroke. Br J
Psychiatry. 1991;158:83–92.
35. Barefoot JC. Depression and coronary heart disease. Cardiologia. 1997;42:1245–1250.[Medline] [Order article via Infotrieve]
36. Barefoot JC, Helms MJ, Mark DB, Blumenthal JA, Califf RM, Haney TL, O’Connor CM, Siegler IC, Williams RB. Depression and long-term mortality risk in patients with coronary artery disease. Am J Cardiol. 1996;78:613–617.[Medline] [Order article via Infotrieve]
37.
Frasure-Smith N, Lesperance F, Talajic M. Depression
following myocardial infarction. JAMA. 1993;270:1819–1825.
38.
Brummett BH, Babyak MA, Siegler IC, Bosworth HB,
Clapp-Channing NE, Siegler IC, Williams RB Jr, Mark DB. Social support
and hostility as predictors of depressive symptoms in cardiac patients
one month following hospitalization: a prospective study.
Psychosom Med. 1998;60:707–713.
39. Levine JB, Covino NA, Slack WV, Safran C, Safran DB, Boro JE, Davis RB, Buchanan GM, Gervino EV. Psychological predictors of subsequent medical care among patients hospitalized with cardiac disease. J Cardiopulm Rehabil. 1996;16:109–116.[Medline] [Order article via Infotrieve]
40. Steffens DC, O’Connor CM, Jiang WJ, Steffens DC, O’Connor CM, Jiang WJ, Pieper CF, Kuchibhatla MN, Arias RM, Look A, Davenport C, Gonzalez MB, Krishnan KR. The effect of major depression on functional status in patients with coronary artery disease. J Am Geriatr Soc. 1999;47:319–322[Medline] [Order article via Infotrieve]
41.
Morris PLP, Robinson RG, Andrzejewski P, Samuels J,
Price TR. Association of depression with 10-year poststroke mortality.
Am J Psychiatry.. 1993;150:124–129.
42. Nelson LD, Cicchetti D, Satz P, Sowa M, Mitrushina M. Emotional sequelae of stroke: a longitudinal perspective. J Clin Exp Neuropsychol.. 1994;16:795–806.
43.
Parikh RM, Robinson RG, Lipsey JR, Starkstein SE,
Fedoroff JP, Price TR. The impact of poststroke depression on recovery
in activities of daily living over a 2-year follow-up. Arch
Neurol.. 1990;47:785–789.
44.
Stern RA, Bachman DL. Depressive symptoms following
stroke. Am J Psychiatry.. 1991;148:351–356.
45. Shima S, Kitagawa Y, Kitamura T, Fujinawa A, Watanabe Y. Poststroke depression. Gen Hosp Psychiatry.. 1994;16:286–289.[Medline] [Order article via Infotrieve]
46.
Starkstein SE, Cohen BS, Fedoroff P, Parikh RM, Price
TR, Robinson RG. Relationship between anxiety disorders and depressive
disorders in patients with cerebrovascular injury. Arch Gen
Psychiatry. 1990;47:246–251
47. Robinson RG, Szetela B. Mood changes following left hemisphere brain injury. Ann Neurol. 1981;9:447–453.[Medline] [Order article via Infotrieve]
48. Dam H, Pedersen HE, Ahlgren P. Depression among patients with stroke. Acta Psychiatr Scand. 1989;80:118–124.[Medline] [Order article via Infotrieve]
49.
House A, Dennis M, Warlow C, Hawton K, Molyneux A. Mood
disorders after stroke and their relation to lesion location: a CT scan
study. Brain. 1990;113:1113–1129.
50. Berkman LF, Leo-Summers L, Horowitz RI. Emotional support and survival following myocardial infarction: a prospective population-based study of the elderly. Ann Intern Med. 1992;117:1003–1009.
51.
Case RB, Moss AJ, Case N, McDermott M, Eberly S. Living
alone after myocardial infarction. JAMA. 1992;267:515–519.
52. Hance M, Carney RM, Freeland KE, Skala J. Depression in patients with coronary heart disease. Gen Hosp Psychiatry. 1996;18:61–65.[Medline] [Order article via Infotrieve]
53. Orth-Gomer K, Unden AL, Edwards ME. Social isolation and mortality in ischemic heart disease. Acta Med Scand. 1988;224:205–215.[Medline] [Order article via Infotrieve]
54.
Williams RB, Barefoot JC, Califf RM., Haney TL,
Saunders WB, Pryor DB, Hlatky MA, Siegler IC, Mark DB. Prognostic
importance of social and economic resources among medically treated
patients with angiographically documented coronary artery
disease. JAMA. 1992;267:520–524.
55. Bosworth HB, Schaie KW. The relationship of social environment, social networks, and health outcomes in the Seattle Longitudinal Study: two analytical approaches. J Gerontol. 1997;52B:P197–P205.
56. Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychol Bull. 1985;98:310–357.[Medline] [Order article via Infotrieve]
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