The association of socio-economic status, race, psychosocial factors and outcome in patients with systemic lupus erythematosus

N. Sutcliffe, A. E. Clarke1, C. Gordon2, V. Farewell3 and D. A. Isenberg

Centre for Rheumatology/Bloomsbury Rheumatology Unit, Department of Medicine, University College London, UK,
1 Divisions of Clinical Immunology/Allergy, Clinical Epidemiology, Montreal General Hospital, McGill University, Montreal, Quebec, Canada,
2 Department of Rheumatology, Division of Immunity and Infection, University of Birmingham, Birmingham and
3 Department of Statistical Science, University College London, London, UK

Correspondence to: N. Sutcliffe, Centre For Rheumatology/Bloomsbury Rheumatology Unit, Arthur Stanley House, 4th floor, 40–50 Tottenham Street, London W1P 9PG, UK.


    Abstract
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Objective. To determine the relationship between socio-economic status, race, psychosocial factors and outcome in patients with systemic lupus erythematosus (SLE).

Methods. One hundred and ninety-five patients with SLE were studied at two centres in the UK (London and Birmingham). Information about sociodemographics, income, employment status, social support and satisfaction with care was obtained. Outcomes were assessed by end-organ damage, disease activity and employment status.

Results. Non-Caucasian race, longer disease duration, higher disease activity and lower level of education were associated with more organ damage in SLE. More satisfaction with access to care and interpersonal aspects of care, but less satisfaction with time spent with doctors, were also associated with more damage. Very long disease duration was associated with higher disease activity. Patients with higher disease activity, lower level of education and from the Birmingham centre were more likely not to be working due to their lupus.

Conclusion. Race and socio-economic status, as well as clinical and psychosocial factors, determine outcome in SLE.

KEY WORDS: SLE, Socio-economic status, Race, Psychosocial factors, Organ damage, Disease activity


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
The survival of patients with systemic lupus erythematosus (SLE) has improved over the last 40 yr from an estimated 5 yr survival of 50% to >90%. The 10 yr survival rate is now nearly 90% [1]. As a consequence, outcome measures other than death are necessary to assess prognosis. It is generally agreed that the prognosis of patients with SLE should be described by three domains: quality of life, disease activity and accumulated damage [2].

We have previously determined the predictors of quality of life in patients with SLE [3]. Several indices have been developed to assess disease activity in SLE. Of these, the Systemic Lupus Activity Measure (SLAM), the British Isles Lupus Assessment Group (BILAG) and the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) have been the most widely used, and have been shown to be valid and reliable [4]. Morbidity in patients with SLE also relates to damage produced in individual organs either as a result of previous inflammation or as a complication of therapy [5]. For this reason, a damage index was developed and validated by members of the Systemic Lupus International Collaborative Clinics (SLICC) [6].

Several disease-related and general demographic characteristics such as gender, race, age at onset, socio-economic status and psychosocial factors have been examined as possible factors affecting prognosis in SLE with somewhat controversial results [7]. Although race has been shown to be important in the development and prognosis of SLE, it has been difficult to separate the effects of race from socio-economic status. Most of the studies that have evaluated this relationship were performed in the USA which has a primarily private health care system. The UK has a primarily publicly funded health care system and, in theory, all patients should have equal access to care, irrespective of income. We therefore felt that it was timely to conduct a study in the UK examining the relationship between socio-economic status, race, psychosocial factors and outcome in patients with SLE. We have assessed the outcome as determined by the SLAM activity, the SLICC damage indices and the employment status in 195 consecutive patients with SLE attending two specialist lupus clinics in London and Birmingham.


    Methods
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
A prospective, cross-sectional study of 195 consecutive patients with SLE attending two specialist lupus clinics in the UK was undertaken (107 patients from the Centre for Rheumatology/Bloomsbury Rheumatology Unit, University College London and 88 from the Rheumatology Department of Birmingham University). These patients were also enrolled in a trinational study of health services utilization and outcomes in SLE [8]. Patients from London were assessed between June and September 1995. Patients from Birmingham were assessed between January and April 1996. Both units used the same protocol. Sociodemographic data including age, sex, race, marital status and educational level expressed as years of education were obtained. Disease duration (calculated from the time patients first fulfilled the revised ACR criteria for the classification of SLE [9]), disease activity [assessed by the revised SLAM (SLAM-R)], and individual and total end-organ damage scores [using the SLICC/American College of Rheumatology Damage Index (SLICC/ACR DI)] were also determined during the same clinic visit. Patients also filled in self-report questionnaires about their social support (Interpersonal Support Evaluation List; ISEL), satisfaction with care (Patient Satisfaction Questionnaire; PSQ), income and employment status (economic questionnaire) [10].

Measures
SLAM was developed by Liang and colleagues in Boston, based on the consensus of members of the Lupus Council of the American College of Rheumatology. It includes 32 items, divided into 11 organ systems, and assigns a degree of severity on a scale of 1–3 with 1 being mild and 3 most severe. The total possible score is 86 [11]. It has been shown to be comparable to other commonly used disease activity indexes including the BILAG and SLEDAI [12]. Its validity and reliability have been shown previously [4, 12]. We have used the revised SLAM (SLAM-R) in this study, which differs very little from the original SLAM [4].

The SLICC/ACR DI is a measurement of cumulative end-organ damage in SLE. It was introduced in 1992. Its validity and reliability have been shown previously [6, 1315]. Damage is described as non-reversible change, not related to active inflammation, occurring since the onset of lupus, ascertained by clinical assessment and present for at least 6 months unless otherwise stated. It is defined for 12 organs or systems: ocular (range 0–2), neuropsychiatric (0–6), renal (0–3), pulmonary (0–5), cardiovascular (0–6), peripheral vascular (0–5), gastrointestinal (0–6), musculoskeletal (0–6), skin (0–3), gonadal (0–1), endocrine damage (0–1) and malignancy (0–2). The maximum possible total score is 46.

Social support was considered as a psychosocial factor which may determine disease outcomes. Social support is a process by which interpersonal relationships promote psychological well-being and protect people from health declines, particularly when they are facing stressful life circumstances. It enables recipients to use effective coping strategies by helping them come to a better understanding of the problem faced, increasing motivation to take instrumental action, and reducing emotional stress, which may impede other coping efforts. In addition, support may encourage the performance of positive health behaviours, thus preventing or minimizing illness and symptom reporting [16]. The ISEL scale has been used to assess social support [17]. It includes scales measuring four social support functions: Belonging (availability of people one can do things with); Appraisal (availability of someone to talk to); Tangible (availability of material aid); Self Esteem (availability of a positive comparison when comparing oneself with others). Each ISEL subscale consists of 10 items with four possible responses (0–3), with a total subscale score range from 0 (least support) to 30 (most support). The total ISEL score is the sum of the four component scores (0–120). This questionnaire has been used previously in a study of patients with SLE in Canada and was shown to correlate with direct and indirect costs of disease, which are another aspect of outcome [10].

We felt that patients' degree of satisfaction with medical care they receive may also be an important psychosocial factor influencing disease outcomes. Patient satisfaction rating is a personal evaluation of health care services and providers. It is intentionally subjective. The Medical Outcomes Study Patient Satisfaction Questionnaire was originally developed by Ware et al. in the USA [18]. We have used Version IV in this study. This questionnaire enquires about global level of satisfaction, considering all health care providers and settings, without specifying a particular provider or hospitalization. It is comprised of seven dimensions, including general satisfaction, technical competence (i.e. diagnosis and management), interpersonal satisfaction (e.g. courtesy and respect), communication satisfaction, time spent with doctor and access to care. The financial dimension was omitted because it poses questions of limited applicability in the British system of health care funding. Each scale is scored between 0 and 100. Higher scores indicate more satisfaction with care. A summary score has not been developed. This questionnaire was chosen because there are no specific patient satisfaction questionnaires developed for UK patients and, of the validated patient satisfaction questionnaires developed in the USA, this was most appropriate for UK patients with the minor modification mentioned above.

Statistical analysis
The analyses were based on ordinal logistic regression models [19]. In such models, there is an ordered outcome variable, Y, which has, say, K categories. Thus, Y can take the values 1, 2, ..., K. Associated with each study subject are explanatory variables X1, X2, ..., Xp which code information about the subject. The relationship between the explanatory variables and Y is specified as: log(probability(Y>j)/probability(Y<=j))=Aj+B1.X1 +B2.X2+...+Bp.Xp. The regression coefficients, the Bi values, correspond to the log odds ratio. Thus, exp (Bi ) represents, for all values of j, the change in the odds of Y being bigger than j vs being less than or equal to j, when the explanatory variable Xi is increased by one unit. For example, if Bi=0.69 corresponding to a variable coded 0 for Caucasian patients and 1 for non-Caucasians, then the model indicates that the odds of being in a higher damage category for non-Caucasians are twice (i.e. e0.69 =2) those of a Caucasian patient.

The ordered outcomes were a grouped version of the SLICC damage scores with six categories (see Table 1Go) and a grouped version of the SLAM scores in three categories (0–4, 5–10, >10). We have also considered employment status as another outcome measure. The outcome variable, not working due to lupus, was a binary classification (not working due to lupus vs working or not working due to other reasons). The explanatory variables were considered in three groups. The first group corresponding to clinical centre, ethnic origin and disease duration (coded as three levels: <10, 10–20, >20 yr) were viewed as baseline variables and were entered first into the model for purposes of adjustment. Their inclusion was not based on any significance tests. Age was not included because of its strong correlation with disease duration. Since the association between damage and disease duration was stronger than that of damage and age, disease duration was included in the models.


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TABLE 1.  Damage categories
 
The second group of explanatory variables corresponding to three socio-economic variables (education level, the ISEL scores and marital status) were first entered singly into an ordinal regression model including the baseline variables and then entered jointly to examine their influence after adjusting for other socio-economic variables. The last group comprises the six dimensions of the PSQ. They were added singly and jointly to the model that included both the baseline variables and the three socio-economic variables. Two of the socio-economic variables, years of education and the ISEL scores, have both a natural ordering and a large range. To avoid undue influence being given to a few extreme values, these variables were recoded into a small number of ordered categories. The education categories were 5–9, 10–14, 15–19 and >20 yr. The ISEL categories were <50, 50–74, 75–100 and >100. The ordered variables were then entered into the model as a single covariate, thus assuming that the effect of these variables, if present, would be linear on this ordered scale. To aid in comparison, the PSQ variables were all standardized to have mean 0 and S.E. 1. Thus, the odds ratios calculated for these variables correspond to the effect associated with a change of one sample standard deviation in the variable.


    Results
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
A total of 184 patients (94.4%) were female. Since there were only a small number of male patients (11), the analysis was restricted to the female patients. Of these female patients, 101 (54.9%) were from the London centre and 83 (45.1%) were from Birmingham; 76.6% were Caucasian, 11.9% were Afro-Caribbean, 9.7% were Asian; 62.1% were married. Median age was 38.9 yr (range 20–80), median disease duration was 9 yr (range 1–39). Median years of full-time education were 12 (range 6–27). Details of patients' employment status and income categories are shown in Table 2Go. Almost half of the patients did not work and about a third of the patients declared that they did not work because of their lupus. The disease characteristics of these patients are described in Table 3Go.


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TABLE 2.  Employment and income categories
 

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TABLE 3.  Disease characteristics of patients
 
The results of fitting the baseline model for the SLICC and the SLAM response variables are presented in Table 4Go. Longer disease duration and non-Caucasian race appear to be related to damage. There is only a suggestive relationship between race and activity. Very long disease duration and activity appear to be weakly related. No obvious centre effects are evident in this analysis.


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TABLE 4.  The results of fitting the baseline model for the outcome variables
 
Table 5Go represents the addition, to the models in Table 4Go, of the socio-economic variables. The upper section of the table corresponds to adding the variables singly and in the latter they are included together. There is some suggestion of a relationship between education and damage. The evidence is stronger when the effect of education is examined after adjusting for the other socio-economic variables. Other analyses indicate that it is the correlation between the ISEL scores and education which is responsible for the change (this analysis is not shown). Thus, there is some indication that higher education levels are associated with less damage. There is no relationship between the socio-economic variables and activity.


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TABLE 5.  The results of addition of the socio-economic variables to the baseline model
 
Table 6Go adds the various satisfaction variables to a model with both the baseline and the socio-economic variables. When added singly, none of the satisfaction variables demonstrate any relationship with damage or activity. In a multivariate model, when the variables are added together, there is still no relationship between patient satisfaction and activity. However, three of the satisfaction scales, interpersonal, time spent with doctor and access to care, demonstrate significant relationships with damage. Greater satisfaction with interpersonal aspects of care and with access to care are associated with higher levels of damage. Greater satisfaction with time spent with the doctor is associated with lower levels of damage.


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TABLE 6.  The results of addition of the satisfaction variables to a model with both the baseline variables and the socio-economic variables
 
The confounding associated with the satisfaction variables, which is evident in these multivariate results, can be illustrated using some simplified variables. Consider a damage variable dichotomized based on a SLICC score of >1 or <=1 and satisfaction variables dichotomized as greater or less than the mean. For patients with `time spent with the doctor' scores less than the mean, a damage rate of 17/54 or 31% is observed for patients with an `interpersonal' score less than the mean and a rate of 9/12 or 75% is observed for those with an `interpersonal' score greater than the mean. For patients with `time spent with the doctor' scores greater than the mean, the same comparison is of damage rates of 4/24 or 17% vs 29/93 or 31% for patients with `interpersonal' scores less than or greater than the mean, respectively.

Thus, for patients with comparable satisfaction concerning `time spent with the doctor', the higher damage rates are seen in those with the highest satisfaction with `interpersonal' aspects of their care. Also, for patients with comparable satisfaction with `interpersonal' aspects of their care, the lower damage rates were seen in patients with the highest levels of satisfaction with `time spent with the doctor'. The correlation between these different areas of satisfaction is evidenced by the fact that 93/111 or 79% of those satisfied with their `time spent with doctor' were satisfied with `interpersonal' relationships, whereas only 12/66 or 18% of those dissatisfied with `time spent with the doctor' were satisfied with `interpersonal' relationships. Failure to adjust for this correlation in the univariate analyses accounts for the different results found in these analyses compared with the multivariate analysis.

Table 7Go represents a final model when all the variables are entered altogether. When SLAM scores are added into the SLICC model in the final step, the association between education and damage becomes stronger (P=0.04). There is also an association between higher disease activity and more damage. The addition of SLAM scores into the final model does not affect the qualitative results. However, the estimated coefficient relating access to care to damage becomes slightly smaller and the associated P value for the test for an association between damage and access to care changes from 0.04 to 0.09. There is also an apparent centre effect after adjustment for socio-economic, patient satisfaction variables and the SLAM.


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TABLE 7.  The results of fitting the final model for all the outcome variables
 
Tables 4, 5 and 6GoGoGo also present results for the `not working due to lupus outcome'. Patients from Birmingham and patients with a lower level of education were more likely to be not working due to their lupus. Otherwise, the results are similar to those based on the SLICC scores, although only the association between more satisfaction with interpersonal aspects of care and not working due to lupus achieves significance. When SLAM scores are added into the final model, there is also an association between higher disease activity and not working due to lupus.


    Discussion
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
This study shows that patients of non-Caucasian origin, with longer disease duration, lower level of education and higher disease activity, are more likely to have end-organ damage.

It has long been known that patients of Afro-Caribbean and Asian origin are at more risk of developing SLE and they tend to have more severe disease [20, 21]. It has, however, been difficult to separate the effects of race from socio-economic status, especially in countries like the USA where access to health care is closely linked to income. Some studies show that American Blacks have a poorer survival than White patients do and this is not due to their socio-economic status [22]. In contrast, in other studies, the association between increased mortality and Black race appears to be related to socio-economic status, which is poorer among Blacks [23]. The discrepancy between these studies may be partly due to the use of unstable measures of socio-economic status, like insurance status. We have confirmed in this study that non-Caucasian patients who, in theory, have equal access to health care as Caucasian patients in the UK, still accumulate more organ damage and this is independent of their level of education. We have also shown that lower level of education is a separate risk factor for organ damage.

We have considered the educational level to be an indicator of socio-economic status in this study. Educational level has previously been shown to be a more stable determinant of socio-economic status compared to other factors like insurance status or income, which change with time and disease onset [24]. Although we have assessed patients' employment status and income, this was done at the time of damage assessment. Ideally, we would have liked to know about these facts prior to disease onset, although there would be a recall bias and we could not be sure about the accuracy of the information given after many years of disease.

At the time of study, almost half of the patients did not work and about a quarter of these patients did not declare their income (in comparison to 7% of employed patients who did not declare their income). Since there was so much missing data on income, it was not included in the analyses. Information on employment status was complete (Table 2Go). About a third of the patients declared that they were not working due to their lupus. Patients with a lower level of education were more likely not to be working due to their lupus. Since the prospects of employment would be expected to be worse for the less educated even in the absence of illness, this finding was not surprising. Patients from Birmingham were also more likely to be not working due to their disease, perhaps due to relatively more adverse economic circumstances. Other factors may have played a part in determining the employment status of those patients. Not surprisingly, patients with higher disease activity were also more likely not to be working due to their lupus.

Longer disease duration and, to a lesser extent, higher disease activity were both associated with more damage in this study. It has previously been shown by Karlson et al. [7] that cumulative damage is strongly associated with non-modifiable clinical factors such as older age at diagnosis, longer disease duration, nutrition and higher disease activity. They did not find any relationship between race, socio-economic status and damage, which is in contrast to our study. Their study was conducted in the USA which has a different social, education and health care system. Their cohort was also somewhat different from ours. They had a higher percentage of Afro-Caribbean patients (52% vs 11.9% in our study). Their patients were also younger (mean age 37.6 vs 40.3 yr) and had a shorter duration of disease (mean disease duration 3.8 vs 10.3 yr). The percentage of unemployed patients was 7% (at diagnosis) and 16% (at study visit) in their cohort vs 49.5% at study visit in ours. All these factors may have a role in explaining the discrepancies between the two studies.

Multivariate analyses also showed associations between damage and some aspects of patient satisfaction with care. Patients with more damage were more satisfied with interpersonal aspects of their care (e.g. courtesy and respect) and their access to care (how easily and quickly they can be seen in clinic, get medical care in an emergency or be admitted), but less satisfied with the amount of time they spent with their doctor. Patients who were not working due to their disease were also more satisfied with the interpersonal aspect of their care. Although it is difficult to predict the direction of these relationships, these results probably imply that patients with more damage need to spend more time with their doctors either during a consultation or during treatment, and this aspect of our care needs improvement. These results partly reflect our practice and partly reflect our patients' expectations.

There was weak evidence for an association between very long disease duration and higher disease activity at the time of assessment. Other variables including sociodemographic, socio-economic and patient satisfaction variables did not appear to be correlated with disease activity. Karlson et al. [24] previously showed that having higher education and private insurance or Medicare were the best predictors of less disease activity at diagnosis. We have studied the relationship between education level and disease activity at the time of study and failed to show any significant relationship. Karlson et al. also showed that race is not significantly associated with disease activity at diagnosis, even when other socio-economic factors are considered. In our study, there was only a suggestion of a relationship between non-Caucasian race and higher disease activity. In a similar cohort, they have also shown that younger age at diagnosis and psychosocial factors like lower self-efficacy for disease management and less social support were associated with greater disease activity at study visit [7]. We did not confirm this relationship between social support and disease activity.

In a recent study, Reveille et al. [25] studied the impact of immunogenetic and socio-economic factors on SLE in three different ethnic groups (Caucasians, African-Americans and Hispanics). They have shown that African-American and Hispanic patients and patients with less education have higher disease activity at disease onset. They have also shown an association with HLA status. These authors also studied the same cohort early in the course of their SLE (disease duration <=5 yr) and showed an association between disease activity and African-American ethnicity, lack of private health insurance, as well as other clinical, immunological, immunogenetic, behavioural and psychological variables [26]. Some of the discrepancies between studies may be due to the assessment of disease activity at different time points during the course of disease (at disease onset vs early or late in the course of SLE) or different health care and education systems.

In summary, end-organ damage was increased in patients of non-Caucasian race and low socio-economic status. Other predictors of damage included clinical factors like disease duration and activity, and also some aspects of satisfaction with care. In contrast, increased disease activity was only associated with long disease duration and none of the sociodemographic, socio-economic or psychosocial factors were predictive of disease activity in this cross-sectional study. As well as controlling disease activity by conventional treatment, improving the level of education may reduce end-organ damage in patients with SLE.


    Acknowledgments
 
We gratefully acknowledge Dimitria Panaritis, Jean Heath and Stephanie Heaton for their technical assistance, Bruce Bovill at the SAS Institute, Maidenhead, UK, for his support and advice, Lupus UK and the West Midlands Lupus Group for their financial support in Birmingham.


    References
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 

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Submitted 16 February 1999; revised version accepted 25 May 1999.