The impact of low family income on self-reported health outcomes in patients with rheumatoid arthritis within a publicly funded health-care environment{dagger}

C. A. Marra1,2,3, L. D. Lynd4,5, J. M. Esdaile3,6, J. Kopec1,3 and A. H. Anis1,2,3

1 Department of Health Care and Epidemiology, University of British Columbia, 2 Centre for Health Evaluation and Outcome Sciences, 3 Arthritis Research Centre of Canada, 4 Program for Assessment of Technology in Health, McMaster University, 5 Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario and 6 Division of Rheumatology, University of British Columbia, Vancouver, BC, Canada.

Correspondence to: A. H. Anis, MHA Programme, Department of Health Care and Epidemiology, 620B-1081 Burrard Street, Vancouver, British Columbia, Canada V6Z 1Y6. E-mail: aslam.anis{at}ubc.ca


    Abstract
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Objective. Self-rated health (SRH) is an independent, strong predictor of morbidity and mortality. Socio-economic status (SES) is strongly associated with SRH. This study investigated the relationship between SES and SRH outcomes in a sample of patients with rheumatoid arthritis (RA) in Canada.

Methods. Both generic preference-based [Health Utilities Index Mark 3 (HUI3) and Short Form 6D (SF-6D)] and non-preference-based [disease-specific (Rheumatoid Arthritis Quality of Life, RAQoL) and a functional status (Health Assessment Questionnaire, HAQ)] SRH questionnaires were administered to 313 RA patients. Both proximate (education and annual household income) and contextual (neighbourhood income, education and unemployment) measures of SES were captured. Ordinary least squares (OLS) regression was used to adjust for RA severity while assessing the relationship between SRH and SES measures. Two-stage least-squares (TSLS) regression was used to determine if there was an inter-relationship between SES and SRH measures.

Results. The sample was well distributed across RA severity and SES measures. Contextual and proximate measures of SES were poorly correlated. Lower levels of proximate SES measures (but not contextual) were associated with poorer SRH outcomes. The OLS regressions showed significant associations between the HUI3 and the SF-6D overall scores and the HAQ for self-reported income. The RAQoL did not differ significantly across SES. TSLS regression confirmed the finding that self-reported income was similarly associated with the SRH measures.

Conclusions. Even in a country with universal access to health-care, the impact of a chronic disease such as RA on SRH is associated with self-reported income. The finding that preference-based measures vary with income independently of RA severity could bias economic evaluation.

KEY WORDS: Socio-economic status, Self-reported health, Preferences, HRQL, HUI3, SF-6D, RAQoL, HAQ


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
As comprehensively outlined by recent reviews, it has been well established that self-rated health is an independent, strong predictor of morbidity and mortality [1, 2]. Consequently, self-reported health outcomes are increasingly being assessed in the evaluation of chronic disease states, such as rheumatoid arthritis (RA) [3, 4]. Socio-economic status (SES) has been shown to be strongly associated with self-reported health, and therefore more attention is being directed towards its determination and its role in the development and progression of disease [5].

Preference-based generic health-related quality of life (HRQL) instruments are often used as self-reported measures of health and are increasingly used in economic evaluations as weighting factors for quality-adjusted life years (QALYs). Similarly, disease-specific HRQL and functional status measures are often used as self-reported measures to fully assess the impact of chronic diseases and as monitoring tools in clinical practice. Despite the well-known association between self-reported health outcomes and SES, there has been little work evaluating the impact of SES on the results obtained using preference-based and disease-specific HRQL measures in patients with RA.

In Canada, there is a universal health-care system that is governed by the principles outlined in the Canada Health Act. However, despite the principles of public administration, universality, accessibility, portability and comprehensiveness outlined in the Act, there remain large socio-economic inequalities in health within our system [6, 7]. In British Columbia, these disparities have been investigated in well-established, chronic diseases, such as asthma [8] and HIV/AIDS [9]. However, to our knowledge, the role of SES in self-reported health outcomes experienced by those with RA in a North American country with universal health-care has not been investigated to date.

Therefore, the purpose of this study was to investigate the relationship between SES and self-reported health outcomes (both preference-based generic and disease-specific HRQL and functional status) in a sample of patients with RA. Our hypothesis was that, despite adjustment for measures of disease severity, self-perceived generic and disease-specific HRQL and functional status would be worse in patients of lower SES.


    Methods
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Study sample/design
Three hundred and thirteen English-speaking adults between 19 and 90 yr of age diagnosed with RA by a rheumatologist and who resided in the Greater Vancouver Regional District or the rural Okanagan region of British Columbia were recruited into this cross-sectional study. One hundred and ninety-seven (63%) patients were recruited directly by study rheumatologists and 116 were recruited via targeted mailouts to individuals with RA identified by their rheumatologists. The University of British Columbia Behavioural Research Ethics Board approved the study protocol, and written, informed consent (according to the Declaration of Helsinki) was obtained from each participant.

Generic health-related quality of life measurement
Both the Short Form 6D (SF-6D) and the Health Utilities Index Mark 3 (HUI3) were used to measure generic HRQL, both of which have been shown to have cross-sectional construct validity in patients with RA (C. A. Marra, J. C. Woolcott, K. Shojania, R. Offer, J. Kopec, J. E. Brazier, J. M. Esdaile and A. H. Anis, submitted for publication). Since these instruments measure different dimensions/attributes, both were included to assess a broader range of possible health outcomes [10]. We also used the Health Utilities Index Mark 2 (HUI2) and the EuroQol (EQ-5D) to assess generic HRQL; however, since the dimensions assessed by these instruments are largely captured by the SF-6D and the HUI3 [10], results obtained with these instruments are not reported.

Brazier et al. created the SF-6D to derive a preference-based measure of health from the Short Form-36 [11]. The SF-6D measures six dimensions, each with four to six levels, and includes physical functioning, role limitation, social functioning, pain, mental health and vitality. A total of 18 000 health states can be defined by this classification system. Of these states, 249 were valued using the standard gamble in a sample of 611 UK participants from the general population. Modelling was used to generate the remainder of the values for the health states. Thus, in the final model, the boundaries of the SF-6D multi-attribute utility values were +0.30 and 1.00 (where 0 is death and 1.00 is perfect health). The minimal important difference (MID) is thought to be about 0.03 (C. A. Marra, J. C. Woolcott, K. Shojania, R. Offer, J. Kopec, J. E. Brazier, J. M. Esdaile and A. H. Anis, submitted for publication) [12].

The HUI3 was created initially to measure HRQL in the National Population Health Survey [13]. The eight attributes of the HUI3 are: vision, hearing, speech, ambulation, dexterity, emotion, cognition and pain. Preference scores can be calculated for each attribute. The HUI3 system uses five or six levels for each attribute, giving a total of 972 000 possible unique health states [14]. The boundaries of the overall multi-attribute utility score on the HUI3 are –0.36 to 1.00, and the MID is thought to be about 0.06 (C. A. Marra, J. C. Woolcott, K. Shojania, R. Offer, J. Kopec, J. E. Brazier, J. M. Esdaile and A. H. Anis, submitted for publication).

Functional status measurement
The Health Assessment Questionnaire (HAQ) [15] was one of the first self-reported, functional status (disability) measures developed and has become one of the dominant instruments in musculoskeletal diseases including RA [15]. The HAQ has been used to assess functional status for approximately two decades and is a common outcome measure for clinical trials in RA. The HAQ is a measure of physical disability that assesses a respondent's ability to complete everyday tasks in areas such as dressing and grooming, rising, eating, walking, personal hygiene, reach, grip and other activities (such as getting into and out of a car). Each of these areas is assigned a section score that is further adjusted to account for the use of any aids, devices or help from another person. These are then summed and averaged to give an overall score between 0.0 (best possible function) to 3.0 (worst function). A change in HAQ score of 0.25 is considered to represent the MID [16, 17].

RA-specific quality of life measure
The Rheumatoid Arthritis Quality of Life (RAQoL) questionnaire [18] is a newly developed instrument and is the first patient-completed instrument specifically designed for use with RA patients [18]. It was derived directly from qualitative interviews with relevant patients and considers aspects of many areas of life that are detrimentally affected by RA. The RAQoL is meant to be a comprehensive, disease-specific scale that will be more responsive to change than previous scales used in RA. The RAQoL consists of 30 questions with binary responses that assess such aspects of RA as moods and emotions, social life, hobbies, everyday tasks, personal and social relationships, and physical contact. The RAQoL is scored by assigning one point for each affirmative response and no points for negative responses. Thus, scores vary from 0 (best RA-specific quality of life) to 30 (worst RA-specific quality of life). We have estimated that the MID for the RAQoL is approximately 1.7 (C. A. Marra, J. C. Woolcott, K. Shojania, R. Offer, J. Kopec, J. E. Brazier, J. M. Esdaile and A. H. Anis, submitted for publication).

Clinical measurements
In addition to demographic questions, participants were asked questions regarding their RA management and severity, including DMARD and prednisone therapy over the past 3 months. These questions included swollen and tender joint count (using the mannequin-based 42 joint count method) [19], 10 cm pain visual analogue scale (VAS), and five-point Likert scales of self-perceived RA severity and the control and duration of RA. The erythrocyte sedimentation rate (ESR) was obtained from the health record. Questions examining health-care services utilization over the previous year, such as hospitalization, use of other professional services (physiotherapy, occupational therapy, home care, massage therapy, etc.), and the rental or purchase of physical aides (walker, wheelchair, cane, etc.) were also included.

Socio-economic status
The association between SES and generic and disease-specific HRQL was tested at both the individual and population level of SES. Individual measures of SES were based on self-reported annual household income and education. Annual household income was classified as less than $20 000, from $20 000 to $50 000, and greater than $50 000 (Canadian dollars) [8]. Since number of people living in each household can have an impact on annual household income, this variable was included in all analyses. Education was classified based on the number of years of post-secondary education and the highest level of education completed, ordinally categorized as less than high school, high school or trade diploma, or at least a university bachelor's degree.

Using a postal code conversion file, we determined the census tract where each participant's current residence was located. From this we determined the neighbourhood characteristics related to SES for each participant's current residence that could be derived from the province's (British Columbia) census data [20]. Census tract level variables deemed representative of SES included median neighbourhood income, the proportion of the population over 20 yr of age completing at least a bachelor's education, and the neighbourhood unemployment rate.

Statistical analysis
Spearman's {rho} was used to examine the correlations between the different measures of SES (values of {rho} greater than 0.50 or less than –0.50 were considered strong; values between –0.49 and –0.30 or between 0.30 and 0.49 were considered moderate; values between –0.30 and 0.30 were considered weak) [21]. The dependent variables for all other analyses were the global utility scores for the SF-6D and the HUI3, the single-attribute utility scores for the HUI3 and SF-6D, and the overall RAQoL and HAQ disability score. Univariate associations between the dependent variables and demographic characteristics (age and gender), disease severity measures (duration of RA, self-reported pain, swollen/tender joint count, self-reported severity and control) and measures of SES were assessed using simple linear regression. Comparisons of self-reported health and RA severity measures across categories of SES were conducted. Statistical comparisons were made using analysis of variance (ANOVA), Student's t-test or the {chi}2 test, as appropriate.

As both the RAQoL and HAQ yield ordinal scores and have been shown to fit the item response theory one-parameter Rasch measurement model [18, 22], we fitted the data from these scales to the Rasch model, and used these linear transformations as the dependent variable for all regression analyses involving the scores of these scales.

For the primary analysis, ordinary least squares (OLS) regression was used to adjust for RA clinical measures and then to assess the relationship between SES and the dependent variables. Each SES variable was modelled separately. All two-way interactions between SES and RA clinical measures were also tested in the multiple regression models. Model fit was assessed using adjusted R2 and standardized residuals were plotted against standardized predicted scores to assess each model for homoscedasticity.

To account for the possibility that there is an inter-relationship between the dependent variables (the generic or disease-specific HRQL or functional status score) and annual household income, two-stage least-squares (TSLS) regression was used. For TSLS, the problematic predictor variable (in our case, income) must be continuous rather than categorical. Therefore, for the TSLS analysis, the self-reported annual income variable was converted to a continuous measure in increments of $10 000.00 (as reported in the original questionnaire). Instrumental variables are not influenced by others in the model but have influence on the variable of interest. Thus, for the first stage of the regression, the instrumental variables used in our analysis to predict annual household income were marital status, number of people in the household, and educational status, which were all highly correlated with income but lowly correlated with HRQL or functional status measures. In the second stage, the predicted values of income were regressed on the generic HRQL scores (HUI3 or SF-6D), the disease-specific HRQL scores (RAQoL) and the HAQ scores to yield unbiased parameter estimates. These variables were compared with OLS regression coefficients (from models using the same annual household income variable) to determine how closely they matched.


    Results
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 Abstract
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 Methods
 Results
 Discussion
 References
 
Characteristics of the study participants are presented in Table 1. The average age of the sample was 61.5 ± 25.9 yr, there were more women (79%), and males tended to be older (66 vs 60 yr, P = 0.004). The mean number of years since being diagnosed with RA was 13.9 ± 11.4. Fifty-eight (19%) of the 313 participants did not report their annual household income and were therefore excluded from the income analysis at the individual level. To determine if this subgroup was different from the group that reported annual income levels, we compared the values for the variables presented in Table 1 between these two groups. There were no differences between these variables in the two subgroups, adding confidence that no bias was introduced through these missing data.


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TABLE 1. Characteristics of the study participants (n = 313)

 
The sample was well distributed across levels of SES. Although 50 (16%) had an annual household income below $20 000, 47 (15%) reported incomes over $70 000, 15 of which exceeded $100 000. There was a significant relationship between self-reported annual income and number of household members, 56% of those reporting an income of less than $20 000 per year being the only household member compared with 17 and 9% of those reporting annual household incomes of $20 000–50 000 and more than $50 000, respectively (P<0.0001). The mean number of household members was also significantly lower for those with an annual household income reported as less then $20 000 (mean 1.7, S.D. 1.1), compared with those reporting annual household incomes of more than $50 000 per year (mean 2.6, S.D. 1.4, P<0.0001).

With respect to self-reported education, 114 (51%) had completed at least 1 yr of post-secondary education (median 1.0) and 52 (17%) had received at least a bachelor's degree; 32 (10%) completed at least 5 yr of post-secondary education. The sample was also heterogeneous for the contextual measures (neighbourhood median income, prevalence of having received at least a bachelor's degree, and percentage neighbourhood unemployment), the percentage of participants residing in neighbourhoods with unemployment rates ranging from 1% to 18% (median = 9%) and the prevalence of a bachelor's degree ranging from 2% to 52% (median = 12%).

The self-reported measures of SES (annual income and education) were moderately correlated ({rho} = 0.33, P<0.0001). However, the contextual measures tended to be more highly correlated amongst themselves, with correlation coefficients ranging from 0.56 to 0.73 (all P<0.0001). Correlations between self-reported and contextual measures were mostly low, with coefficients ranging from 0.11 to 0.31 (all P<0.05).

Unadjusted associations between the generic HRQL measures (the SF-6D and the HUI3), or the RAQoL and the HAQ, and demographic, SES and RA severity variables are presented in Appendices 1 and 2, available as supplementary data at Rheumatology Online, respectively. There were no associations between age and either of the generic HRQL measures; however, there was a significant positive association with the HAQ disability index (P = 0.02). Men had significantly better generic and disease-specific quality of life and functional status, as measured by all the instruments. Most of the RA severity variables were significant across all of the HRQL instruments and the HAQ. No associations were found between contextual measures of SES (neighbourhood median income, prevalence of bachelor's degrees, and proportion of neighbourhood unemployed) and any of the generic or disease-specific HRQL measures or the HAQ (Appendices 1 and 2, available as supplementary data at Rheumatology Online).

For both self-reported income and education levels, comparison of mean values of all measures (SF-6D, HUI3, RAQoL, HAQ) by ANOVA showed a significant gradient across SES categories (Table 2). For example, lower levels of income were associated with poorer generic and disease-specific HRQL and physical function. Results were confirmed with non-parametric tests (Kruskal–Wallis). In general, all measures of health-care services utilization (hospitalization, use of professional services, and use of physical aids/equipment), joint damage and health status showed a consistent gradient of worse functioning in those in lower self-reported SES categories (self-reported annual family income and self-reported education level) (Table 2). There were no differences across SES categories for type or number of DMARDs used or the use of prednisone over the past 3 months (data not shown). There were no gradients or associations between any of these variables and the contextual measures of SES (data not shown).


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TABLE 2. Comparison of RA and health status measures across different social classes.

 
For the proximate (self-reported) SES measures, the differences in these variables were statistically significant for most of the subjective RA severity measures (self-reported RA severity, patient global assessment of disease activity) and both the global scores of the generic and disease-specific quality of life measures. Of note, most of the physically based clinical measures (such as joint counts) were not significantly different between the different SES levels, suggesting no physical differences in disease severity. For the single attributes of the generic quality of life scores, those hypothesized to be most relevant to RA or older individuals tended to differ significantly across SES categories. For example, for the SF-6D single-attribute scores, physical functioning, role limitations, social functioning, pain and vitality were all significantly higher in higher social classes defined by income or education. For the HUI3 single-attribute scores, there were significant differences for hearing, speech, ambulation and dexterity across SES defined by income categories. For categories based upon education, significant differences existed for speech and ambulation.

The results of the OLS regressions with adjustment for disease severity measures show significant associations between the HUI3 and the SF-6D overall scores and the HAQ for self-reported income (Figs 1 and 2). There were no other significant associations for other measures of SES (self-reported education of contextual measures) after adjustment for disease severity. To account for differences in household size across self-reported annual household income categories, we included the number of people in the household in the regression models. Other measures of disease management and severity that were tested but did not improve the overall fit of the model included the number and type of DMARDs used within the past 3 months, the number of other chronic diseases, the swollen joint count (collinear with tender joint count) and the ESR. Of note, the RAQoL did not differ significantly across self-reported income categories. All differences in the ß estimates between the lowest and highest income categories exceeded the MID for the SF-6D, HUI3 and HAQ.



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FIG. 1. Generic HRQL by self-reported annual income. The points indicate the least-squares means and the lines indicate their 95% confidence intervals. HRQL measures were adjusted for RA duration, pain VAS, self-reported RA control and severity, tender joint count, RAQoL score, and number of people in the household. Higher scores indicate better HRQL.

 


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FIG. 2. RAQoL score and HAQ disability index by self-reported income. The points indicate the least-squares means and the lines indicate their 95% confidence intervals. Scores were adjusted for RA duration, pain VAS, self-reported RA control and severity, tender joint count, patient global assessment of RA activity, and number of people in the household. Lower scores indicate better HRQL/functional status.

 
The results of the TSLS regression analyses also revealed that self-reported annual income was significantly associated with generic HRQL (P = 0.03 and 0.04 for the HUI3 and SF-6D, respectively) and the HAQ (P = 0.002) but not for the RAQoL (P = 0.07). A comparison of the ß coefficients between OLS and TSLS regression revealed close agreement for the HUI3 (0.03 for both), the SF-6D (0.01 for both), the HAQ (–0.09 vs –0.08) and the RAQoL (–0.50 vs –0.70). Thus, there was probably little, if any, feedback between income and HRQL or functional status in the sample, adding further support for the utilization of OLS.


    Discussion
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
This study demonstrates a consistent and significant gradient in both generic and disease-specific HRQL and functional status and other self-reported health measures across income and education categories. This gradient is maintained even after adjustment for RA severity and the number of people in the household for self-reported income (but not education) categories. Of note, there was no significant gradient across SES measures for physically defined RA severity measures (disease process and joint damage measures). Thus, these results highlight how SES affects a well-defined chronic disease such as RA by influencing how patients perceive and report their health status.

These findings become particularly important when one considers that self-rated health predicts mortality even after controlling for a wide range of factors (demographic, psychosocial, prior illness, physician's assessments and physiological measures) [23]. Thus, from our results, we have determined that low SES predicts poor self-reported health independently of RA severity and may thus be a strong contributing factor to the early mortality and substantial morbidity seen in RA patients with low SES [24, 25].

Another important finding is that the magnitudes of utility values assessed by both the HUI3 and the SF-6D vary by SES independently of RA severity measures. This finding has potentially important ramifications for the results of cost–utility analyses in therapies for RA as investigators need to ensure balance between treatment groups, not only in clinical and disease-specific factors but also in SES. Therefore, to avoid potential confounding or bias due to SES status in economic evaluation, one would need to: (i) verify SES at baseline in randomized controlled trials (RCT) in which utility measures will be used in a cost–utility analysis, and use stratified randomization to ensure balance between the arms; (ii) control for SES in observational studies in which such measures may be used; and (iii) ensure that the results obtained from the sample will be generalizable to the population of interest (i.e. to the extent that studies do not report SES, or the SES is not similar to that of the general population).

Another concern about the association between income and global utility scores in preference-based measures that we observed has been summarized by Sculpher and O’Brien [26]. These authors point out that people who complete preference-based instruments may have ill health that will have resulted in an actual drop in their income (as with our sample). Consequently, their responses to some items in the instruments may partly reflect this income loss. The authors argue that this finding might have important implications for cost–utility analysis, such as double counting and impairment of between-country comparisons. For the double-counting problem, the authors describe a situation in which, in the valuation of a preference-based instrument, the income effects of a health state have been incorporated by the reference respondents but the field respondents of the same instrument have incorporated the health effects of reduced income in their responses. For problems in cross-country comparisons of economic evaluations, there may be differences between countries in income maintenance programmes for those with ill health, which could potentially create differences in health benefits of similar interventions.

In patients with RA, while it is well established that there are associations between low SES and morbidity and mortality, the mechanisms behind these associations are largely unknown. Callahan et al. [27] reported that scores on a helplessness scale appeared to mediate a component of the association between formal education level and 5-yr mortality. In a study attempting to identify a partial explanation for the association between low education and poor outcome in patients with RA, Katz [28] showed that self-care was strongly associated with education, and thus concluded that low education was a proxy for a constellation of factors responsible for poor health outcomes. Therefore, the differences in self-reported health that we observed on both the generic and disease-specific HRQL and the HAQ scales might be indicative of helplessness or inability to complete self-care tasks in patients with lower SES.

Our results generally support the findings by Brekke et al. [29] and McEntegart et al. [30], who showed that self-reported health outcomes, but not objective indices of disease activity, differed across groups based upon SES. Specifically, McEntegart et al. [30] revealed how patients living in more deprived areas in Scotland had poorer HAQ scores than those living in more affluent areas. Similarly, Brekke et al. [29], who conducted their study as a comparison of RA patients from affluent west Oslo with those from deprived east Oslo, extended these findings to disease-specific and generic quality of life measures. Both of these analyses used contextual measures of SES. The study by McEntegart et al. used the Carstairs index (a composite score using postal codes that draws on measures of overcrowding, male unemployment, social class and car ownership) while Brekke et al. used neighbourhood factors (such as income, education, employment, mortality, housing standard and proportion of Third World citizens) to define the two areas of Oslo as affluent or deprived.

Our findings build on those previously reported by including multiple measures of SES, including those directly reported by the patient as opposed to performing neighbourhood level analyses and the addition of two preference-based, generic HRQL instruments and the RAQoL. Since we collected patient-specific RA drug treatment data, we were able to determine that there were no treatment differences across SES categories that could have influenced self-reported outcomes. Similarly, since all of our subjects were under the care of rheumatologists, any differences in specialist vs non-specialist care that may have been due to SES and could potentially have influenced self-reported outcomes were avoided.

In addition, our study is the first to examine if this relationship holds true in a North American country with universal access to health-care. Of note, we adjusted our model by the number of people living in the household. While we found that there was a significant difference in number of people per household across self-reported annual incomes, higher levels of income being reported by those with larger families, this variable was not significantly associated with the self-reported health variables, did not affect the magnitude or significance of the association of annual household income with the dependent variables, and did not significantly improve the multiple linear regression model fit.

Another point of interest that arises from the results of our study was the lack of a consistent gradient (except for the HUI3) across the income categories for the adjusted models of self-reported generic and disease-specific HRQL and functional status (Figs 1 and 2). For example, with the SF-6D, it appears that the biggest difference across income categories is between the middle and highest groups rather than between the lowest and highest groups. These results raise the possibility that there may not be a perfect gradient across the three separate income categories and that it may be a dichotomous phenomenon (i.e. high vs low income), an annual household income cut-off of approximately $50 000 defining the two groups. Another possible explanation is that there is another factor that is somehow influencing the self-reported health outcomes for the middle income category, making it lower than both the high and low income categories.

Of interest, in our study there was a low correlation between the proximate (self-reported) and contextual measures of SES. With both annual household income and education, there were strong associations with self-reported health. However, once adjustments for RA severity were made, only self-reported annual income remained significant. A possible explanation for the lack of association between the self-reported HRQL measures and education and contextual SES measures is that they may not be indicative of SES in elderly populations, such as those with RA. Our sample mostly consisted of patients who had worked in an era when there was less emphasis on education. Therefore, in contrast to a younger, employed sample of asthmatics from the same geographical area, where education and income were highly correlated [8], results from our sample revealed that these two variables were less correlated. Similarly, in the aforementioned asthma sample, there were strong correlations between contextual and proximate measures of SES that were not observed in our RA patient sample, indicating that these measures of SES may be more robust for younger participants who are more likely to be currently employed. Another finding from our sample that supports this premise is that older individuals (>50 yr of age) who were still in the work force tended to have less education but similar income to those working individuals less than 50 yr old. Another possible explanation of this observation is that neighbourhood characteristics are poor proxies for individual characteristics. For example, there might be almost as much variation among individuals within a neighbourhood as there is between neighbourhoods.

While there were significant gradients across SES (as defined by annual household income) for both of the generic HRQL measures and the HAQ after adjustment for RA severity, similar findings were not observed for the RAQoL. Despite significant univariate gradients across SES as defined by annual household income and education, the RAQoL did not display a clear SES gradient in the multiple linear regression analysis. We postulate that the reason for this is that the RAQoL is capturing items that are so germane to RA that the variance in its score is explained mostly by the objective and subjective disease severity measures. Indeed, the addition of annual household income had a negligible impact on the model R2 in the multiple linear regression analysis of the RAQoL, whereas it improved the model fit in all the other analyses.

Finally, it can be argued that the results using OLS regression reveal an association between self-reported annual household income and HRQL or functional status without the ascertainment of directionality (i.e. whether low income causes low HRQL/functional status or vice versa). We used TSLS regression to account for this and found no evidence to support the idea that the low income was ‘caused’ by the low HRQL or functional status (i.e. the ß coefficients achieved by OLS were not biased). This finding probably makes sense in our sample since most participants were elderly and retired and their current annual household income was probably not influenced by their current HRQL or functional status.

Our study shows that even in a country such as Canada, with universal access to health-care, the impact of RA on self-reported health is strongly associated with SES as measured by annual income even after adjusting for disease severity. Because self-reported health has been strongly associated with mortality and morbidity, there are important implications for intervention. In addition, these findings should be considered in the context of cost–utility analysis to prevent biasing of utility values obtained from preference-based instruments. In the event that studies do not investigate or report SES or if the SES in the study sample differs significantly from the population of interest, the results of the analysis may have poor generalizability. Further research should focus on the mediating factors that contribute to this social gradient in self-reported health outcomes in patients with RA.


    Acknowledgments
 
The authors appreciate the reviews by Mr John Woolcott and Dr Amir Adel Rashidi, which strengthened the paper. CAM is supported by a Canadian Institute of Health Research/Arthritis Society fellowship and a Michael Smith Foundation for Health Research Training Award. This work was supported by a competitive research grant from the Canadian Arthritis Network, a Network of Centres of Excellence.

The authors have declared no conflicts of interest.

Supplementary data

{keh334i1}

    Supplementary data are available

    at Rheumatology Online.


    Notes
 
{dagger}Presented in part as a poster at the International Society of Quality of Life Research Annual Meeting in Prague, Czech Republic, November 2003. Back


    References
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 

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Submitted 17 October 2003; revised version accepted 28 June 2004.



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