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

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 Mea- sure), 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, Mat- thew H. Liang, MD, MPH Harvard Medical School, Robert B. Brigham Multipurpose Arthritis and Musculoskeletal Diseases 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 Hospi- tal, Boston, Massachusetts; Jodi Grosflam, M D Harvard Medical School, Boston, Massachusetts; W. Neal Roberts, MD. Medical Col- lege 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. Ka- vanaugh, MD. University of Texas Southwestern Medical Center at Dallas.

Address reprint requests to Elizabeth W. Karlson, MD, Division of RheumatologyAmmunology, Brigham and Women’s Hos- pital, 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 (P 5 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 (P 5 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 (P S 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, attribu- tion of risk to socioeconomic factors does little to

48 KARLSON ET AL

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 1 study 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 geographi- cally 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 At diagnosis At study visit

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

Nutrition Preventive health behaviors

Behavioral

Disease activity (by SLAM) Damage (by SLICC score) Health status (by SF-36)

Yes No No No

No No

No Yes Yes Yes Yes No

No No No No No No

No No Yes Yes No

Yes Yes Yes Yes

Yes Yes

Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes

* SLAM = SLE Activity Measure; SLICC = Systemic Lupus Interna- tional 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. Al- though the mechaliisms of action are not well understood, low SES has been associated with variables such as lack of re- sources, 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

$20,000-$29,999, $30,000-$50,000, or >$50,000). Income measures constructed for our analysis included the midpoint

(<$5,000, $5,000-$9,999, $10,000-$14,999, $15,000-$19,999,

ROLE OF SES, RACE, AND MODIFIABLE RISK FACTORS IN SLE 49

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 diagnosis and 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-established factor in the reduc- tion 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 differences in 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 ac- curate, 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). Knowl- edge 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

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 self- efficacy (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 Multidimen- sional 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 conditions were 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

50 KARLSON ET AL

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 Sys- temic 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 collec- tion 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 determi- nants 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. pothesized association with outcome*

Relationship of predictor variables, organized by the hy-

Tertiary Secondary Primary variable variable variable

Sex Race

Center Income at diagnosis Occupation at diagnosis Insurance at diagnosis Employment at diagnosis Education

Age

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.


Table 3. Characteristics of the study patients (n = 200)* Table 4. Variables associated with disease activity and damage in 200 patients with systemic lupus erythematosus (SLE)

Outcome, variable P T Characteristic At diagnosis At study visit

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

PrivateiMedicare, % patients Medicaid, % patients Uninsured, % patients

Insurance

52 93

33.8 (13.1) 7

46.4 (13.4)

22,700 (18,600) 32,400 (27,400)

25

ND ND

37.6 (12.9) 16

48.1 (13.2)

24,000 (18,700) 31,000 (23,800)

25.5

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

0.0001 0.0006 0.003

0.006

4.7 3.5 3.0

2.8

0.03 0.03 0.04 0.04

2.2 2.2 2.1 2.0 ND

ND ND

18 35 47

0.0001 0.0001 0.009 0.02

10.5 4.4 2.6 2.4

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). 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-

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.

Table 5. Variables associated with worse physical function in 200 patients with systemic lupus erythematosus

Variable P T

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

Alcohol abstinence Below poverty level at study visit Below poverty lcvel at diagnosis Medicaidino insurance at study

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

More cormorbid conditions

diagnosis

visit

study visit

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

RESULTS 0.0001 0.0002 0.0002 0.0002

4.1 3.9 3.8 3.8

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 Med- icaid 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

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.

52 KARLSON ET AL

Table 6. Variables associated with worse mental health status in 200 patients with systemic lupus erythematosus

Variable P T

Lower self-efficacy 0.0001 10.1 Less social support 0.0001 6.3 Lower income at study visit 0.0001 6.3 Below poverty level at study visit 0.0001 5.8 Stronger chance locus of control 0.0001 5.7 Less education 0.0001 5.8 Lower income at diagnosis 0.0001 5.6 Less network social support 0.0001 5.5 Weaker internal locus of control 0.0001 5.3 Below poverty level at diagnosis 0.0001 5.2 Medicaidin0 insurance at diagnosis 0.0001 4.7 Medicaidino insurance at study visit 0.0003 3.7 Greater disease activity at diagnosis 0.0006 3.5 Stronger “god” locus of control 0.0006 3.5

Less compliance 0.002 5.0*

Lower occupational prestige at 0.001 3.2 study visit

Black race 0.004 2.9 Center 0.004 4.4* Less frequent dental care 0.02 6.7* Stronger “powerful others” locus of 0.01 2.6

control Lower occupational prestige at 0.01 2.5

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 _______~ ~ _ _ _ _ _ ~

Greater disease activity* Lower self-efficacy Less social support Younger age at diagnosis

Older age at diagnosis Longer duration of SLE Lower caloric intake Greater disease activity at

Lower occupational prestige

Greater damage?

diagnosis

at diagnosis

0.06 3.40 0.07

0.05 0.24 0.81 0.05

0.03

0.0001 0.0041 0.0060

0.0001 0.0001 0.0018 0.0061

0.0010

* 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 Lower % protein in diet 0.01 2.5 Greater damage at diagnosis 0.02 2.3 models. Variables selected from the hierarchical models Lower satisfaction 0.04 2.1 as predictors of greater disease activity at study visit

included lower self-efficacy for disease management Less knowledge 0.05

* Determined by the F-test. (P 9 0.0001), less social support (P < 0.005), and younger age at diagnosis (P < 0.007) (Table 8). Besides

diagnosis-

2.0

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 Pear- son 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

SLAMISLICC SF-36 mentaliphysical function SLAMISF-36 physical function SLAMISF-36 mental health SLICCISF-36 physical function SLAWSLICC SLICCISF-36 mental health

r

0.21 0.64

-0.47 -0.40 -0.21

0.18 -0.07

P

0.0033 0.0001 0.0001 0.0001 0.0022 0.01 1 0.30

* See Table 1 for definitions.

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-efficacy for 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


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 P P

Worse physical function* Lower self-efficacy Abstinence from alcohol Less knowledge Less education Less social support

Lower self-efficacy Less social support Below poverty level at study

Less knowledge Stronger “god” locus of conti Greater disease activity at

Worse mental health status*

visit?

diagnosis

0.66 11.81 10.28 1.45

10.52

0.50 18.39 11.19

11.31 -01 2.52

0.54

0.0001 0.0001 0.0127 0.0162 0.0495

0.0001 0.0002 0.0010

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

(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 self- efficacy 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 indepen- dently associated with survival.

Socioeconomic factors have their most profound effect not on disease activity, but on a person’s physical,

54 KARLSON ET AL

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, resigna- tion, 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 benefi- cial 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

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). How- ever, we found no significant relationship between self- reported 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/physician visits”).

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 low- calorie 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-


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-efficacy for 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 Mi-

kail, Kay Morgan, Elain Davis, Jackie McFarlin, and Sarah Breitbach for recruiting and interviewing patients and coordi- nating the study at the collaborating centers, and Dr. Jeffrey Katz for reviewing the manuscript.

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Documents

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