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The relationship of socioeconomic status race and modifiable risk factors to outcomes in patients with systemic lupus erythematosus.

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ARTHRlTlS & RHEUMATISM
Vol. 40,No. 1, January 1997, pp 47-56
8 1997, American College of Rheumatology
47
THE RELATIONSHIP OF SOCIOECONOMIC STATUS, RACE, AND
MODIFIABLE RISK FACTORS TO OUTCOMES IN PATIENTS WITH
SYSTEMIC LUPUS ERYTHEMATOSUS
ELIZABETH W. KARLSON, LAWREN H. DALTROY, ROBERT A. LEW, ELIZABETH A. WRIGHT,
ALISON J. PARTRIDGE, ANNE H. FOSSEL, W. NEAL ROBERTS, STEVEN H. STERN,
KARIN V. STRAATON, MARY C. WACHOLTZ, ARTHUR F. KAVANAUGH,
JODI M.GROSFLAM, and MATTHEW H. LIANG
Objective. To study the relationship of race, socioeconomic status (SES),clinical factors, and psychosocial factors to outcomes in patients with systemic
lupus erythematosus (SLE).
Methods. A retrospective cohort was assembled,
comprising 200 patients with SLE from 5 centers. This
cobort was balanced in terms of race and SES.Patients
provided information on socioeconomic factors, access
to health care, nutrition, self-efficacy for disease management, health locus of control, social support, compliance, knowledge about SLE, and satisfaction with
medical care. Outcome measures included disease activity (measured by the Systemic Lupus Activity Measure), damage (measured by the SLICC/ACR damage
index), and health status (measured by the SF-36).
Supported in part by NIH grants AR-36308, AR-39921,
AI-07306, and AR-07530, and an Arthritis Foundation Investigator
Award.
Elizabeth W. Karlson, MD, Elizabeth A. Wright, PhD, Matthew H. Liang, MD, MPH Harvard Medical School, Robert B.
Brigham Multipurpose Arthritis and MusculoskeletalDiseases Center,
and Brigham and Women’s Hospital, Boston, Massachusetts; Lawren
H. Daltroy, DrPH: Harvard Medical School, Robert B. Brigham
Multipurpose Arthritis and Musculoskeletal Diseases Center, Brigham
and Women’s Hospital, and Harvard School of Public Health, Boston,
Massachusetts; Robert A. Lew, PhD, Alison J. Partridge, LICSW,
Anne H. Fossel: Robert B. Brigham Multipurpose Arthritis and
Musculoskeletal Diseases Center, and Brigham and Women’s Hospital, Boston, Massachusetts; Jodi Grosflam, M D Harvard Medical
School, Boston, Massachusetts; W. Neal Roberts, MD. Medical College of Virginia, Richmond; Steven H. Stern, M D University of
Louisville, Louisville, Kentucky; Karin V. Straaton, M D University of
Alabama at Birmingham; Mary C. Wacholtz, MD, Arthur F. Kavanaugh, MD. University of Texas Southwestern Medical Center at
Dallas.
Address reprint requests to Elizabeth W. Karlson, MD,
Division of RheumatologyAmmunology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA 02115.
Submitted for publication November 6, 1995; accepted in
revised form July 15, 19%.
Results. In multivariate models that were controlled for race, SES, center, psychosocial factors, and
clinical factors, lower self-efficacy for disease management (P5 O.OOOl), less social support (P< 0.005), and
younger age at diagnosis (P < 0.007) were associated
with greater disease activity. Older age at diagnosis (P
5 O.OOOl), longer duration of SLE (P5 O.OOOl), poor
nutrition (P < 0.002), and higher disease activity at
diagnosis (P< 0.007) were associated with more damage. Lower self-efiicacy for disease management was
associated with worse physical function (P S 0.OOOl)
and worse mental health status (PS 0.OOOl).
Conclusion. Disease activity and health status
were most strongly associated with potentially modifiable psychosocial factors such as self-efficacy for disease management. Cumulative organ damage was most
highly associated with clinical factors such as age and
duration of disease. None of the outcomes measured
were associated with race. These results suggest that
education and counseling, coordinated with medical
care, might improve outcomes in patients with SLE.
The relationship of race and socioeconomic status (SES) to poor outcomes in patients with systemic
lupus erythematosus (SLE) has been debated for more
than 20 years. Six studies have shown an association
between lower SES and higher morbidity or mortality in
patients with SLE (1-6). In addition, 2 studies have
shown higher morbidity and mortality in black patients
with SLE (3,7). However, the association of race and
SES with outcome was confounded by SES in 3 other
studies ( 1 3 ) .
Whether or not race and socioeconomic factors
are independent risk factors for poor outcomes, attribution of risk to socioeconomic factors does little to
KARLSON ET AL
48
elucidate the mechanisms that contribute to poorer
health (9). The identification of modifiable risk factors
would have major implications for patient care and
public health, but, thus far, only 1study has attempted to
do this (8). Herein, we report the results of a multicenter
study designed to limit the confounding of race and SES
by assembly of a patient cohort that had equivalent
distributions of social and economic variables within
each racial group. Several psychosocial and socioeconomic factors presumed to affect the health of these
SLE patients were then studied.
If confounding can explain the findings of previous studies, then there should be little relationship
observed between race and outcomes, unless there are
unmeasured confounding variables present or there are
interactions between race and other variables. Our
hypothesis for the present study was that variation in
outcomes could be explained by psychosocial or behavioral variables alone, and that neither race nor assessments of SES would be associated with outcomes after
controlling for these variables.
PATIENTS AND METHODS
Design. A group of patients with SLE, balanced in
terms of race and SES, was assembled to minimize confounding. Patients were obtained from 5 centers and had been
initially seen within 2 years of disease onset (all had <7 years
duration of disease). All patients met the American College of
Rheumatology (ACR) criteria for SLE (10). After informed
consent was secured, patients were examined by a rheumatologist to assess disease activity. In addition, all patients were
interviewed to collect pertinent data, as detailed below. The
study was approved by the Institutional Review Boards at all 5
centers.
Selection of patients. The 5 centers were geographically diverse academic units that had well-characterized populations of black and white patients with SLE of varying
socioeconomic class. We selected these sites to limit the impact
of variability in medical care. Potential subjects were identified
from SLE registries and from the clinical or billing records at
each center. We studied patients with early disease to limit the
effects on social factors that result from the disease, thus
allowing us to focus on the variables of interest.
Patients at each center were randomly selected from 1
of 4 racehocioeconomic strata: black race/Medicaid or no
insurance; black raceiprivate insurance or Medicare; white
racelMedicaid or no insurance; or white race/private insurance
or Medicare. A center-specific randomization scheme was
used because strata were of different sizes among the centers.
Of 1,298 patients screened for eligibility, 1,010 did not meet
entry criteria: 447 did not meet the ACR criteria for SLE, 488
had a disease duration longer than 7 years, 44 had a first visit
>2 years from disease onset, 5 were <18 years of age, 3
reported mixed race, and 23 resided outside the catchment
area. Of the 288 eligible patients whom we attempted to
Table 1. Time of assessment of the variables for 200 patients with
systemic lupus erythematosus (SLE)*
Variable
Demographic
Age
Race
Sex
Center
Clinical
Comorbid illnesses
Duration of SLE
Socioeconomic
Education
Employment
Income
Insurance
Occupation
Access
Psychosocial
Compliance
Knowledge
Health locus of control
Satisfaction
Self-efficacy
Social support
Behavioral
Nutrition
Preventive health behaviors
Disease activity (by SLAM)
Damage (by SLICC score)
Health status (by SF-36)
At diagnosis
At study visit
Yes
No
No
No
Yes
Yes
Yes
Yes
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
* SLAM = SLE Activity Measure; SLICC = Systemic Lupus International Collaborating ClinicsiAmerican College of Rheumatology damage index; SF-36 = MOS 36-item short-form health survey.
recruit, 10 had died in the interim, 40 were unreachable
despite multiple followup attempts, and 38 had refused
participation, which resulted in a cohort of 200 patients
(participation rate 70%).
Measures. Socioeconomic factors. Socioeconomic factors are a cluster of related variables, including income,
education, employment, occupational prestige, and type of
health insurance. Those factors have been associated with
health outcomes in numerous studies and populations. Although the mechaliisms of action are not well understood, low
SES has been associated with variables such as lack of resources, crowding, poor diet, lack of access to medical care,
and poor self-care skills, all of which can reduce host resistance
and ability to cope with chronic disease (11,12). In the present
study, these data were assessed by patient interview at study
visit. Patients were asked about current socioeconomic factors
and were asked to recall socioeconomic factors from the time
of diagnosis of SLE (Table 1).
Education (in years) was assessed at study visit, and was
categorized as either incomplete high school, completed high
school, or education beyond high school. Income was measured
as total annual household income at diagnosis (by patient
report) and at study visit, falling into 1 of 7 categories
(<$5,000, $5,000-$9,999, $10,000-$14,999, $15,000-$19,999,
$20,000-$29,999, $30,000-$50,000, or >$50,000). Income
measures constructed for our analysis included the midpoint
ROLE OF SES, RACE, AND MODIFIABLE RISK FACTORS IN SLE
income of the category adjusted for number of persons in the
household and expressed in 1991 dollars, and a dichotomous
variable, which was based on the adjusted income, for income
above or below the national poverty level (13). Occupational
prestige at diagnosis and at study visit was scored using the
National Opinion Research Center occupational prestige
scale, which covers occupational codes used in the 1980 US
census (14). Employment sram (employed or unemployed) at
diagnosis and at study visit was scored separately from occupational prestige. Insurance status at diagnosisand at study visit
was defined as private insurance andlor Medicare, Medicaid,
free care, uninsured, or self-pay.
Psychosocial and behavioral factors. Psychosocial and
behavioral factors may affect health directly (e.g., smoking,
taking medications) andlor indirectly (e.g., through immune
function) (15). We measured factors that affected the course of
chronic disease, as determined by patient interview at study
visit (Table 1).
Preventive health behaviors such as smoking history,
alcohol use, and frequency of nonemergency dental care were
assessed. Since smoking and alcohol use may directly affect
patient health and dental care, these variables served as a
marker of preventive health orientation.
Poor nutrition is a well-establishedfactor in the reduction of host immunity, and has been implicated in the poorer
health status of minority groups in the US (16-18). Individual
dietary elements such as omega-3 fatty acids may suppress
SLE disease activity (19-21). General nutrition, adequacy of
diet, and intake of free fatty acids were measured by the Food
Frequency Questionnaire (22). The validity of this measure has
been documented in the general population (23) and in black
and white low-income pregnant women (24).
Several factors contribute to the adequacy of medical
management and self-management. We assumed that the
medical care provided by the SLE specialists in a referral
center was state-of-the-art, and differencesin practice patterns
would contribute to the “center” effect. Patient variables that
may affect morbidity include accessibility of care, knowledge
of when to seek treatment, compliance with medication regimens and appointments, satisfaction with one’s doctors, and
confidence (self-efficacy) in one’s ability to manage symptoms.
Finances and distance may be barriers to timely or
adequate care. Access to care was measured indirectly by
availability and type of health insurance, and by distance from
home to the rheumatologist’s office. Noncompliance with
treatment has been associated with worse outcome (25).
Patient self-report of compliance, though not completely accurate, is reported to be a practical measure of behavior
(25-27). Compliance was measured by a modified instrument
developed by Morisky and colleagues (26), in which the
behaviorial indices of taking medication, filling prescriptions,
and missing doctor appointments were elicited by patient
interview. The summary scale used has been associated with
hypertension control and mortality within a racially mixed
population of indigent patients with hypertension (28). Knowledge of when to seek treatment for SLE was measured by
asking patients how quickly they would seek help for 14
mild-to-serious symptoms of SLE. A higher knowledge score
indicated that the patients were more likely to seek help
quickly for serious symptoms. Satisfaction with medical care
49
was measured with a modified Medical Interview Satisfaction
Scale (29).
Confidence in one’s ability to perform specific behaviors has been operationalized by Bandura as perceived selfefficacy (30). High self-efficacy is associated with greater and
more tenacious effort with eventual success in behavioral
performance and predicts health outcome (30-33). Self-eficacy
was measured with the “Other Symptoms” subscale from the
Arthritis Self-Efficacy Scale, a valid and highly reliable instrument that measures a person’s sense of confidence in his or her
ability to control daily symptoms in rheumatic disease, which
we reworded for SLE management (34).
Sense of control over one’s life has been associated
with health behaviors, and with both physical and mental
health outcome (35). Sense of control is known to be diminished in persons of low SES and in minority subjects (36).
Locus of control beliefs were measured using the Multidimensional Health Locus of Control scales (37), encompassing the
dimensions of “powerful others,” “chance,” and “internal,”
and augmented by inclusion of an additional scale, ‘god,’’
which assessed how strongly an individual felt that god controlled his or her health. These scales have good internal
reliability in both black and white populations.
Social support is the extent to which the social environment meets an individual‘s interpersonal needs, and has a
significant impact on health and mortality (38-40). Several
dimensions of support, including size and number of social
contacts, were assessed. Emotional and instrumental help
considered necessary to deal with stressful situations was
assessed by questions and subscales from the Social Support
Scale and the Establishment of Populations for Epidemiologic
Study of the Elderly (38,39).
Clinical factors. Number and type of medications and
comorbid conditions were determined by the examining physician at study visit. Comorbid conditionswere defined as those
that could not be directly attributable to SLE or its manifestations, but that could be related to its treatment (e.g.,
hypertension from steroid therapy). Duration of SLE was
calculated as the date of diagnosis subtracted from the date of
study visit.
Outcome measures. Disease activity was measured by
the SLE Activity Measure (SLAM),a physician-rated, valid,
and reliable index (41,42), which ranges from 0 (no disease
activity) to 84 (maximum disease activity).The SLAM score at
diagnosis was assessed by a rheumatologist by medical record
review. SLE clinical evaluations at diagnosis were not done as
part of a study and, therefore, 123 patients (62%) had at least
1 missing laboratory test necessary to complete the SLAM.
The SLAM score at study visit was assessed by a rheumatologist who performed a patient evaluation, blinded to SES
information,and laboratory tests were obtained for all patients
(Table 1).
To study the effect of missing laboratory data on
S L A M scores, a S L A M score was computed after exclusion of
the laboratory data for all patients. This was done for both
SLAM scores at diagnosis and SLAM scores at study visit (with
complete information). Pearson correlations were performed
between SLAM scores and SLAM scores without laboratory
data. SLAM scores at study visit showed a high correlation
with SLAM scores at study visit without laboratory data (r =
0.85, 95% confidence interval [95% CI] = 0.81-0.88). SLAM
KARLSON ET AL
50
scores at diagnosis showed a high correlation with SLAM
scores at diagnosis without laboratory data (r = 0.87, 95%
CI = 0.83-0.91). The total SLAM score at diagnosis was used
as a predictor variable that incorporated all available laboratory data, with missing data coded as 0 for normal in all
subsequent analyses. The total SLAM score at study visit, with
complete laboratory data, was used as an outcome.
Cumulative organ damage was measured by the Systemic Lupus International Collaborating Clinics/ACR (SLICC)
damage index (43). This measure has excellent content, face,
discriminant, and criterion validity and reliability. The SLICC
scores at diagnosis and at study visit were assessed by medical
record review, by a rheumatologist who was blinded to SES
information (Table 1).
Health status was measured at study visit by the MOS
36-item short form health survey (SF-36) (44), an extensively
used, valid, and reliable measure (Table 1). Global mental
health status encompasses depression, anxiety, and impact of
emotions on work and social roles (mean value of mental
health, social functioning, and role-emotional subscales).
Global physical function encompasses physical limitations and
their impact on work and social roles (mean value of physical
functioning, bodily pain, and role-physical subscales) (45).
Data quality management. To standardize data collection across centers, interviewers from 5 sites were trained
centrally using SLE patients for interviews and reinforcement
training every 12 months. Random taping of interviews was
done to provide feedback and to ensure interrater reliability.
To monitor patient accrual and adherence to the protocol, and
to maintain interrater reliability, study physicians met yearly,
examined volunteer SLE patients and their charts, completed
SLAMS and SLICC measures, and discussed discrepancies.
Statistical analysis. Using Cohen’s approach (46), a
sample of 200 yields 80% power to detect an increment of 5%
in R2,controlling for 19 variables that cumulatively explain
35% of the variance, with the a conservatively set at 0.05120 =
0.0025. If 25% of the variance is explained, then we have 80%
power to detect a 6% change in R2; if 50% is explained, we
have 80% power to detect a 4% increment in R2. Predictors
included psychosocial, behavioral, clinical, and sociodemographic factors, SLAM at diagnosis, and SLICC score at
diagnosis. Outcomes (measured at study visit) included disease
activity (by SLAM), cumulative organ damage (by SLICC
score), and self-reported physical function and mental health
(by SF-36). Pearson correlations were computed among the 4
principal outcomes in the study: SLAM, SLICC score, SF-36
global mental health status, and SF-36 global physical function.
We used a staged approach to model-building (Table
2), based on the assumption that behavioral, social, and
psychological factors would be the most immediate determinants of health. These included variables such as sense of
control, compliance with medical regimens, nutrition, and
social support. These variables may be the most amenable to
interventions in the clinic setting, if not at the social level. For
this reason, we entered these first in the model building.
Secondary variables, or those that act through primary variables, were entered next. These included comorbidity, income,
occupation, health insurance, and employment status. For
example, compliance (a primary variable) might be affected by
lower income or lack of health insurance (secondary variables),
but the immediate reason for poor outcome might be non-
Table 2. Relationship of predictor variables, organized by the hypothesized association with outcome*
Tertiary
variable
Secondary
variable
Primary
variable
Sex
Race
Age
Center
Income at diagnosis
Occupation at diagnosis
Insurance at diagnosis
Employment at diagnosis
Education
Disease activity at
diagnosis (by
SLAM)
Damage at
diagnosis (by
SLICC score)
Duration of SLE
Comorbidity
Income at study
visit
Occupation at
study visit
Insurance at study
visit
Employment at
study visit
Access
Compliance
Knowledge
Health locus of
control
Nutrition
Preventive health
behaviors
Satisfaction
Self-efficacy
Social support
~
* See Table 1 for definitions.
compliance and only secondarily because the patient did not
have money to buy the medicine.
The final set of variables entered into the model were
immutable, or distal, factors such as sex, race, age, education,
or center. We entered these into the model last for 2 reasons.
The first is that to find that blacks or whites differ in morbidity
or disease course is providing a label, but not an explanation.
The second reason is that many important predictors are
subsumed with variables such as sex and race. For instance,
race includes different cultural outlooks, patterns of behavior,
and nutrition. When global attributes such as race, sex, and
education are put into a model first, they may dominate the
model because they correlate with other measured and unmeasured variables in the model and mask associated variables
from entering the model. The influence of tertiary variables,
after controlling for primary and secondary variables, probably
represents the effect of other unmeasured correlates of the
tertiary variable. In race, for example, this might represent
biologic differences.
Thus, sets of factors were studied sequentially in a
multiple linear regression model. For each health outcome, we
added sets of factors to a general linear model according to a
hypothesized chain of causality (Table 2). First, we tested
psychosocial and behavioral variables. Then, we added clinical
variables and socioeconomic factors. Finally, we added sex, race,
age, center, and socioeconomic factors at diagnosis of SLE.
Exploratory analyses using linear regression models
were performed to validate these results, and to check for
alternative models that might explain the outcome as well as or
better than the pre-specified null hypothesis model. If other
models do perform as or more effectively, the null model is
discredited. If alternative models do not predict as well, the
null model is considered to be robust. A nominal P value of
0.05 or less was considered significant for reporting, but
readers may wish to use a more conservative criterion, e.g.,
0.0025 (0.05/20), to guard against false positives due to multiple testing.
ROLE OF SES, RACE, AND MODIFIABLE RISK FACTORS IN SLE
Table 3. Characteristics of the study patients (n
Characteristic
Black, % patients
Female, 9% patients
Mean age (SD), years
Unemployed, % patients
Mean NORCt
Income, in US dollars
Mean (SD)
Mean adjusted (SD)?
Below poverty level, %
patientsf
Education, % patients
Incomplete high school
Completed high school
Beyond high school
Insurance
PrivateiMedicare, % patients
Medicaid, % patients
Uninsured, % patients
=
200)*
At diagnosis
At study visit
52
93
33.8 (13.1)
7
46.4 (13.4)
ND
ND
37.6 (12.9)
16
48.1 (13.2)
22,700 (18,600)
32,400 (27,400)
25
24,000 (18,700)
31,000 (23,800)
25.5
ND
ND
ND
18
35
47
63
15
23
60
30
10
* ND = not determined; NORC = National Opinion Research Center
occupational prestige scale.
t Adjusted to 1991 dollars, family size of 4.
$ Below national poverty threshold (adjusted for year and family size).
Residuals and influence points were examined as well.
Terms were introduced to determine if self-efficacy and social
support interacted with race, education, compliance, and
knowledge, and to determine the effect of these measures on
health outcome. Stratification by center and by race was done
to see if the major factors in the full models remained
significant, and to see if new factors were apparent within these
subgroups. For selected outcomes, we examined disease activity at study visit, organ damage at study visit, and number of
medications to see how these affected prediction. Surrogate
(or proxy) variables were sought by excluding the most significant factors one by one to see which, if any, other factors
replaced them (47). Deleting factors one by one makes it
possible to investigate whether a different model having a
different interpretation emerges. When a new plausible model
emerges, it suggests that the original model is neither unique
nor particularly robust.
RESULTS
Sample characteristics. The sample comprised
200 patients, of whom 186 (93%) were female and 104
(52%) were black (Table 3). Mean duration of disease
was 3.8 years. At diagnosis of SLE, 126 patients (63%)
had private or Medicare insurance, 30 (15%) had Medicaid insurance, and 46 (23%) were uninsured. At study
visit, 120 patients (60%) had private or Medicare insurance, 60 (30%) had Medicaid insurance, and 20 (10%)
were uninsured. Other socioeconomic factors are shown
in Table 3.
Correlates of disease activity and damage.
Greater disease activity at study visit was associated with
51
Table 4. Variables associated with disease activity and damage in
200 patients with systemic lupus erythematosus (SLE)
Outcome, variable
Greater disease activity
Lower self-efficacy
Less social support
Below poverty level at
diagnosis
Stronger internal locus of
control
Black race
Lower income at study visit
Lower income at diagnosis
Lower occupational prestige
at study visit
Greater damage
Greater damage at diagnosis
Older age at diagnosis
Longer duration of SLE
Lower caloric intake
P
T
0.0001
0.0006
0.003
4.7
3.5
3.0
0.006
2.8
0.03
0.03
0.04
0.04
2.2
2.2
2.1
2.0
0.0001
0.0001
0.009
0.02
10.5
4.4
2.6
2.4
lower self-efficacy for disease management, less social
support, and income below poverty level at diagnosis
(Table 4). Greater organ damage at study visit was
associated with greater organ damage at diagnosis, older
age at diagnosis, and longer duration of SLE.
Correlates of health status. Worse physical function (assessed by SF-36 global physical function) was
associated with lower self-efficacy for disease manage-
Table 5. Variables associated with worse physical function in 200
patients with systemic lupus erythematosus
Variable
P
Lower self-efficacy
Lower income at study visit
Weaker internal locus of control
Less social support
Less education
Lower income at diagnosis
Medicaidino insurance at
diagnosis
Alcohol abstinence
Below poverty level at study visit
Below poverty lcvel at diagnosis
Medicaidino insurance at study
visit
Less frequent dental care
Stronger chance locus of control
Less network social support
Younger age at diagnosis
Less knowledge
Lower satisfaction
Greater damage at diagnosis
Lower occupational prestige at
study visit
More cormorbid conditions
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
11.1
5.5
5.3
5.2
5.2
4.8
4.1
0.0001
0.0002
0.0002
0.0002
4.1
3.9
3.8
3.8
0.0008
0.001
0.003
0.005
0.005
0.01
0.01
0.03
7.5"
3.3
3.3
2.9
2.9
2.6
2.6
2.1
0.04
2.1
* Determined by the F-test.
T
KARLSON ET AL
52
Table 6. Variables associated with worse mental health status in 200
patients with systemic lupus erythematosus
Variable
P
T
Lower self-efficacy
Less social support
Lower income at study visit
Below poverty level at study visit
Stronger chance locus of control
Less education
Lower income at diagnosis
Less network social support
Weaker internal locus of control
Below poverty level at diagnosis
Medicaidin0 insurance at diagnosis
Medicaidino insurance at study visit
Greater disease activity at diagnosis
Stronger “god” locus of control
Lower occupational prestige at
study visit
Less compliance
Black race
Center
Less frequent dental care
Stronger “powerful others” locus of
control
Lower occupational prestige
at
.~
diagnosisLower % protein in diet
Greater damage at diagnosis
Lower satisfaction
Less knowledge
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0003
0.0006
0.0006
0.001
10.1
6.3
6.3
5.8
5.7
5.8
5.6
5.5
5.3
5.2
4.7
3.7
3.5
3.5
3.2
0.002
0.004
0.004
0.02
0.01
5.0*
2.9
4.4*
6.7*
2.6
0.01
2.5
0.01
0.02
0.04
0.05
2.5
2.3
2.1
2.0
* Determined by the F-test.
ment, lower income at study visit, and weaker internal
locus of control (Table 5). Worse mental health status
(assessed by SF-36 global mental health) was associated
with lower self-efficacy for disease management, less
social support, and lower income at study visit (Table 6).
Correlation among outcome measures. The Pearson correlation between disease activity and organ damage at diagnosis was 0.21 (P 9 0.0033). For outcomes at
study visit, Pearson correlations ranged from 0.07 to 0.64
among disease activity, organ damage, global mental
health, and global physical function (Table 7).
Table 7. Pearson correlation among outcome measures in 200 patients with SLE*
Time of
assessment
Diagnosis
Study visit
Study visit
Study visit
Study visit
Study visit
Study visit
Outcome measure
r
P
SLAMISLICC
SF-36 mentaliphysical function
SLAMISF-36 physical function
SLAMISF-36 mental health
SLICCISF-36 physical function
SLAWSLICC
SLICCISF-36 mental health
0.21
0.64
-0.47
-0.40
-0.21
0.18
-0.07
0.0033
0.0001
0.0001
0.0001
0.0022
0.011
0.30
* See Table 1 for definitions.
Table 8. Multivariate linear regression models for predictors of
disease activity and damage in 200 patients with systemic lupus
erythematosus (SLE)
Outcome, predictor variable
P
P
0.06
3.40
0.07
0.0001
0.0041
0.0060
0.05
0.24
0.81
0.05
0.0001
0.0001
0.0018
0.0061
0.03
0.0010
_______~~ _ _ _ _ _ ~
Greater disease activity*
Lower self-efficacy
Less social support
Younger age at diagnosis
Greater damage?
Older age at diagnosis
Longer duration of SLE
Lower caloric intake
Greater disease activity at
diagnosis
Lower occupational prestige
at diagnosis
* The group of predictors included terms for age, sex, disease activity
at diagnosis, disease duration, disease damage at diagnosis, comorbidities, socioeconomic status variables, and race.
-F The group of predictors included terms for age, sex, disease activity
at diagnosis, disease duration, comorbidities, socioeconomic status
variables, and race.
Predictors of disease activity in multivariate
models. Variables selected from the hierarchical models
as predictors of greater disease activity at study visit
included lower self-efficacy for disease management
(P 9 0.0001), less social support (P < 0.005), and
younger age at diagnosis (P < 0.007) (Table 8). Besides
these 3 factors, no other psychosocial or clinical factors
were significant. Potential confounders such as race,
center, and SES were not significant. The 3-factor model
explained 16% of the variance. The validation analyses
did not substantially alter the main results. When organ
damage at study visit was added as a predictor variable,
the model explained 20% of the variance, the main 3
factors remained significant, and the SLICC scores were
significant (P < 0.003). When we stratified by center, the
effect of self-efficacyfor disease management was nearly
identical at each center, with p coefficients that varied
from 0.04 to 0.10. Patients who failed to complete high
school had significantly less social support (P < 0.005),
but the interaction between social support and education was not significant.
Predictors of organ damage in multivariate models. No psychosocial factors predicted cumulative organ
damage at study visit. The hierarchical models disclosed
5 factors as predictors of greater damage: older age (P 5
0.0001), longer duration of disease (P 5 0.0001), lower
caloric intake (P < 0.002), higher disease activity at
diagnosis (P < 0.007), and lower occupational prestige
at diagnosis (P < 0.001) (Table 8). This model explained
25% of the variance. Disease activity (assessed by
ROLE OF SES, RACE, AND MODIFIABLE RISK FACTORS IN SLE
Table 9. Multivariate linear regression models for predictors of
health status (assessed by the 36-Item Short Form) in 200 patients with
systemic lupus erythematosus
Outcome, predictor variable
Worse physical function*
Lower self-efficacy
Abstinence from alcohol
Less knowledge
Less education
Less social support
Worse mental health status*
Lower self-efficacy
Less social support
Below poverty level at study
visit?
Less knowledge
Stronger “god” locus of conti-01
Greater disease activity at
diagnosis
P
P
0.66
11.81
10.28
1.45
10.52
0.0001
0.0001
0.0127
0.0162
0.0495
0.50
18.39
11.19
0.0001
0.0002
0.0010
11.31
2.52
0.54
0.0044
0.0139
0.0193
* The group of predictors included terms for age, sex, disease activity
at diagnosis, disease duration, disease damage at diagnosis, comorbidities, socioeconomic status variables, and race.
t Below national poverty threshold (adjusted for year and family size).
SLAM) at study visit was an additional significant predictor variable when added to the final model (P <
0.004), which explained 28% of the variance. The validation analyses did not substantially alter the model or
reveal any further significant potential confounders besides disease activity, duration, and age.
Predictors of physical function in multivariate
models. The hierarchical models disclosed 5 factors as
predictors of worse physical function: lower self-efficacy
for disease management (P 5 O.OOOl), abstinence from
alcohol (P 5 O.OOOl), less knowledge about SLE (I‘ <
0.02), less education (P < 0.02), and less social support
(P < 0.05) (Table 9). This model explained 49% of the
variance. The validation analyses did not substantially
alter the model or identify significant potential confounders. When added to the model, disease activity
(assessed by SLAM) at study visit was significant (P 5
0.0001) and the model explained 55% of the variance.
To further investigate the finding that abstinence from
alcohol was associated with worse physical function, we
stratified by education and found that alcohol abstinence
only remained significant for the stratum with education
beyond high school. Alcohol consumption was not predictive among the less educated groups. Other variables
in the model remained qualitatively the same. No effect
was seen with income-related variables.
Predictors of mental health in multivariate models. The hierarchical models disclosed 6 factors as predictors of worse mental health: lower self-efficacy for
disease management (P 5 O.OOOl), less social support
53
(P < 0.0002), below poverty level at study visit (P <
0.002), less knowledge (P < O.OOS), less personal control
over one’s health (P < 0.02), and greater disease activity
at diagnosis (P < 0.02) (Table 9). This model explained
49% of the variance. Disease activity at study visit (P 5
0.0001) was also significant when added to this model
(53% of the variance explained). Otherwise, the validation analyses left the model virtually unchanged.
DISCUSSION
We studied the contributions of psychological,
behavioral, social, and economic factors to health in 200
patients with SLE. The patients were a balanced group
of black and white subjects with a wide range of socioeconomic levels. This allowed us to investigate the
influence of highly specific factors on health that have
been obscured in other studies by global assessments or
weak proxy measures of SES and frequent confounding
of race and SES. We found that disease activity and
health status in SLE were strongly associated with
potentially modifiable psychosocial factors such as selfefficacy for disease management. Self-efficacy alone
explained 22% of the variation in physical health status,
15% of the variation in mental health status, and 7% of
the variation in disease activity. SES had less influence,
and race was not associated with disease activity, cumulative organ damage, or health status. Cumulative organ
damage was strongly associated with non-modifiable
clinical factors such as age and duration of disease and
less strongly associated with one measure of SES, occupational prestige, and with low caloric intake. Disease
activity and health status were strongly correlated, as
one might expect. Cumulative organ damage was only
weakly correlated with these measures and underscores
the fact that it is another dimension of outcome.
As in other chronic diseases, some studies have
demonstrated adverse outcomes in SLE patients of
lower SES (1-6), but others have not had these results
(8,48). Some study findings that have shown adverse
outcomes in black patients were confounded by SES.
The complexity of these relationships was demonstrated
in a followup study of a large SLE cohort, which
demonstrated associations between low SES (measured
as income by census tract and insurance status), but not
race, with higher mortality ( 5 ) . In contrast, a previous
study of the same cohort (4) demonstrated both race and
SES (measured only as insurance status) to be independently associated with survival.
Socioeconomic factors have their most profound
effect not on disease activity, but on a person’s physical,
54
emotional, and social functioning with SLE. Disease
activity can alter SES in a downward spiral, since
lessened ability to work reduces income and health
insurance coverage, resulting in associations between
higher morbidity and lower SES. In the final models
controlling for sociodemographic factors, baseline disease activity, center, and socioeconomic factors (education, poverty status) were associated with health status,
thus confirming the results of previous studies. Even so,
psychosocial factors such as self-efficacy for disease
management and social support were stronger
predictors.
The largely unpredictable disease course in SLE
contributes to considerable uncertainty, fear, resignation, and other dysfunctional behavior (49). Patients
who learn to adjust to the unpredictable nature of SLE
may have better control of their disease and better social
functioning. Lower SES may be associated with increased morbidity, in part because it is associated with
diminished sense of self-efficacy for disease management. In this study, low self-efficacy for disease management in SLE was strongly associated with measures of
morbidity. Self-efficacy for disease management can be
improved by behavioral interventions to either enhance
coping skills or empower patients to participate actively
in monitoring and managing their disease (30-34).
Lower SES is associated with an impoverished
social environment (inadequate instrumental and emotional support), which may provide a pathway between
SES and disease activity. In our study, lower social
support was associated with increased disease activity
and lower mental health. Support groups, ombudsmen
for patients, or a “buddy system” for patients have been
used successfully in other diseases and could be beneficial in SLE (50).
We saw no effect of race on outcome, unlike
previous studies, because the study was designed to
eliminate the confounding of SES with race. We entered
race into our models only after the addition of other
race-related variables that are thought to affect the
outcome more directly. Center characteristics varied
greatly, which underscores the need for multicenter
studies in this area. Although strong center effects were
found in some analyses, the results were confirmed both
by removing the center with the extreme results and by
a separate analysis for each center.
Lower SES may be associated with increased SLE
activity because patients may not have access to quality
care for SLE or be aware that certain symptoms are
potential signs of SLE flare. Inadequate transportation
may prevent and/or inhibit them from seeking care or
KARLSON ET AL
followup. Communication barriers between the patient
and provider and differences in disease perceptions may
limit access or the effectiveness of the health prescriptions. We found that ignorance of when to seek help for
serious symptoms was associated with worse health
status. Other indicators of access, such as health insurance at diagnosis or distance to the physician’s office,
were not significantly associated with outcome. Because
of the design, we cannot comment on quality of care or
other access factors.
In a study of lupus, poor compliance, as defined
by visits kept and physicians’ assessment, was associated
with black race and lower SES; after controlling for race
and SES, physician’s global assessment of compliance
was associated with important renal disease (8). However, we found no significant relationship between selfreported compliance and outcome in our study. These
differences may be due to the way compliance was
defined (we measured compliance by self-report; a physician’s assessment might be biased by race) and to
population differences (the mean values for our patients’ compliance with medications and physician visits
were 3 on a scale of 1-4, where 3 was “hardly ever miss
medications/physicianvisits”).
Lower SES may be associated with more disease
activity because patients with lower SES have inadequate nutrition. Studies in a mouse model of SLE
suggested that diets supplemented with omega-3 fatty
acids decreased renal disease (20), although a controlled
trial in SLE patients demonstrated only a temporary
clinical and serologic benefit (21). The overall intake of
omega-3 fatty acids in our patients was too low to assess
its effect. We found no association of disease activity or
health status with total caloric intake or percentage of
fiber or fat in the diet, or with vitamin A intake. Greater
cumulative organ damage was associated with a lowcalorie diet (<1,200 kcal), but whether worse outcome is
the result of an inadequate diet or is the cause of poor
nutrition cannot be deduced from this cross-sectional
study.
A somewhat surprising association between alcohol abstinence and worse physical function was seen in
the more educated patients. This may be due to an
awareness among persons with more education that
alcohol can have adverse health consequences and further worsen existing health problems.
Some limitations of this study require comment.
The socioeconomic factors at diagnosis, disease activity
at diagnosis, and damage at diagnosis were measured
retrospectively, and the psychosocial factors were measured cross-sectionally. Correlations between psychoso-
ROLE OF SES, RACE, AND MODIFIABLE RISK FACTORS IN SLE
cia1 factors and outcome data were significant, but
causality cannot be certain. A longitudinal study is
needed to validate the findings. Furthermore, the entry
criterion that required patients to meet the ACR criteria
for SLE, the usual convention of clinical studies, could
bias the sample toward sicker patients, and the correlates of outcome identified might not apply to patients
with less severe SLE or early SLE that does not meet the
criteria.
Racial and socioeconomic factors themselves
might be associated with biased recall and self-reported
information. Inequality in recall between blacks and
whites and between patients with lower or higher SES
may lead to specious differences regarding some of the
factors examined in this study. The one we judge most
likely to be affected is the recall of dietary constituents.
Moreover, the potential for socially desirable response
bias among patients of lower SES is always present. We
attempted to minimize acquiescence bias by asking some
questions in the negative and by training interviewers to
be nonjudgmental in their approach and to emphasize
that there are no right answers. Our questionnaires were
pretested for comprehension. Verbal anchors and forced
choices were used in preference to ladder scales, visual
analog scales, or open-ended questionnaires.
SLE is a paradigm of a chronic illness in which
the social environment is thought to be an important
determinant of outcome. The extensive literature on
SLE describes a relationship between lower SES and
poor outcome, but only one study to date has attempted
to identify factors associated with lower SES that are
amenable to change. Our results suggest new social and
behavioral factors that could be modified to improve
outcome. These include psychosocial interventions to
enhance self-efficacyfor disease management and social
support. Self-efficacy for disease management deserves
further study, especially with regard to the direction of
causality and the roots of low self-efficacy. Our findings
have basic methodologic implications for the study of
the social environment and health, and for the improved
management of SLE in socially disadvantaged groups.
ACKNOWLEDGMENTS
We gratefully acknowledge Nova1 Abraham, Isis Mikail, Kay Morgan, Elain Davis, Jackie McFarlin, and Sarah
Breitbach for recruiting and interviewing patients and coordinating the study at the collaborating centers, and Dr. Jeffrey
Katz for reviewing the manuscript.
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