Department of Medicine (Divisions of 1 Rheumatology, 2 Clinical Epidemiology, and 3 Clinical Immunology/Allergy), 9 McGill University Health Centre and Departments of Economics and 10 Biostatistics, 4 McGill University, Montréal, Québec, Canada, Department of Medicine (Division of Rheumatology), Johns Hopkins University School of Medicine, Baltimore, MD, 5 Department of Medicine (Division of Rheumatology) and Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA, 6 Centre for Rheumatology, Department of Medicine, University College London, London, UK, 7 Department of Rheumatology, Division of Immunity and Infection, University of Birmingham, Birmingham, UK, 8 Department of Medicine (Division of Rheumatology), Hôpital Notre-Dame, Université de Montréal, Montréal, Québec, 11 Department of Medicine (Division of Rheumatology) and Department of Epidemiology, University of Toronto, Toronto, Ontario and 12 Arthritis Research Centre of Canada and Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
Correspondence to: Ann Clarke, MD, MSc, Division of Clinical Epidemiology, Room L10-413, Montreal General Hospital, 1650 Cedar Avenue, Montreal, Quebec, Canada H3G 1A4. E-mail: ann.clarke{at}mcgill.ca
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Abstract |
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Patients and methods. Seven hundred and fifteen SLE patients (Canada 231, US 269, UK 215) completed the SF-36 annually over four years. The annual change in the SF-36 Physical and Mental Component Summary (PCS and MCS) scores over the course of the study were summarized by estimating a linear trend for each individual patient using hierarchical modelling. Cross-country comparison of the slopes in the PCS and MCS scores was then performed using simultaneous regressions.
Results. The estimated mean annual changes (95% credible interval [CrI]) in the PCS scores in Canada, the US, and the UK were 0.18 (0.07, 0.43), 0.05 (0.27, 0.17), and 0.03 (0.20, 0.27), respectively; the mean annual changes in the MCS scores were 0.15 (0.04, 0.34), 0.23 (0.09, 0.37), and 0.08 (0.10, 0.27), respectively. Regression results showed that the mean annual changes in PCS and MCS scores did not substantially differ across countries.
Conclusion. Quality of life remained stable across countries. Despite Canadian and British patients incurring lower health costs, on average, patients experienced similar changes in physical and mental well-being.
KEY WORDS: Quality of life, Health status, Systemic lupus erythematosus, Disease damage, Direct healthcare costs
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Introduction |
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Although several measures of quality of life have been studied in SLE, the Medical Outcomes Study Short Form 36 (SF-36) is the most widely accepted [8]. The SF-36 is a generic instrument designed to measure the impact of disease on a patient's physical, social, and psychological function. It has been shown to be internally consistent and to have criterion, construct, and discriminatory validity in patients with SLE [6]. Furthermore, because it is generic, comparisons can be made with other patient groups.
Previous studies pertaining to quality of life in SLE have been limited. Most have been cross-sectional, possibly misrepresenting how patients live with lupus over time. Systemic lupus, characterized by episodes of exacerbation and remission, likely results in varying levels of health status. Two previous longitudinal studies have been performed, but their study populations were small and were recruited from a single centre [9, 10]. In this study, we examined quality of life as expressed by the SF-36 Physical and Mental Component Summary (PCS and MCS) scores over a 4-yr period in patients from six centres in Canada, the US, and the UK.
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Patients and methods |
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Procedures
At study entry and annually, for a maximum of four years, participants completed questionnaires on quality of life, social support, and satisfaction with health care. Also at study entry and semi-annually they reported on health resource utilization. At study entry and conclusion, the patient's treating physician completed disease activity and damage measures.
Study instruments
Quality of life was assessed by the SF-36 [6, 17, 18] and a visual analogue scale (VAS) adapted from the EuroQol [19, 20]. Social support was evaluated through the Interpersonal Support Evaluation List (ISEL) [21] and patient satisfaction through the Medical Outcomes Study Patient Satisfaction Questionnaire (version IV) [22]. Health resource utilization was measured through a modified version of the economic portion of the Stanford Health Assessment Questionnaire [23]. Disease activity was assessed through the SLAM-R [1] and a VAS of current activity and activity over the past year and disease damage through the SLICC/ACR DI [3, 24].
Statistical methods
Demographics, disease characteristics, direct costs, and quality of life were expressed across countries using means and standard deviations (S.D.) and medians, interquartile ranges, and proportions as appropriate. Given the fluctuating nature of disease activity in SLE patients, and hence the variability in their quality of life, comparison of baseline and final values does not reflect the full quality of life experience of these patients. Therefore, to better characterize long-term change in quality of life, all SF-36 PCS and MCS scores over the course of the study were used to estimate the linear trend across time within each individual patient. This was done through two-level hierarchical linear modelling, an approach that allows the borrowing of strength across patients while still allowing for individual within-patient variations [25]. We used the Gibbs sampler as implemented in WinBUGS 1.4 software to estimate the model parameters, with 95% credible intervals (CrI).
For patients who provided incomplete data, (i.e. those who withdrew, were lost to follow up, died, or provided data at entry and conclusion but failed to complete all SF-36 questionnaires), missing PCS and MCS scores were managed through multiple imputation using best predictive regression models with all available data from all patients as potential covariates [26]. Potential covariates included age, sex, ethnicity (Caucasian versus non-Caucasian), education (both as years and categorical as <12 or 12 years), marital status (married versus unmarried), disease duration, health status (individual SF-36 subscales, summary scores, and patient reported VAS), social support (ISEL total score), patient satisfaction with health care (individual subscales), health expenditure, disease activity (both the SLAM-R and physician reported VAS of current activity and activity over the past year), and disease damage. Consistent with our previous analysis [16], for subjects who died during the 4-yr study, imputations were performed up to four years after entry. Alternative modeling strategies, such as omitting deceased patients or including them without performing imputations, would either create a selection bias or make it appear as if death were cost-saving.
A sensitivity analysis was also conducted [27] to account for the possibility of unobserved differences between those providing complete and incomplete data using the following assumptions: (1) multiplying by 0.5 the imputed PCS and MCS scores after the last available data for those who died and by 0.75 for those who withdrew or were lost to follow up, (2) the same as assumption #1, but only for the deceased, (3) the same as assumption #2 and multiplying the imputed PCS and MCS scores by 1.5 for those who withdrew or were lost to follow up. In this way, we provide results for potential differences as large as 50% larger or smaller than those observed.
Cross country comparisons of the patient-specific rate of change in the SF-36 PCS and MCS scores were then performed using simultaneous regressions with indicator variables for the country where the patient was receiving care, with the US as the reference. Only study entry values of the above covariates were considered. These regressions also included as outcomes cumulative health expenditure and damage accumulation over the 4-yr study [16].
For all regressions, model selection was based on Bayes factor as approximated by the Bayesian Information Criteria. This has been shown to have optimal properties for future predictions [28].
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Results |
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When all patients were included by using multiple imputation for those who provided incomplete data, the annual change in the mean PCS score (95% CrI) was 0.18 (0.07, 0.43), 0.05 (0.27, 0.17) and 0.03 (0.20, 0.27), respectively, in Canada, the US, and the UK (Table 2). The annual change in the mean MCS score (95% CrI) was 0.15 (0.04, 0.34), 0.23 (0.09, 0.37), and 0.08 (0.10, 0.27) in Canada, the US, and the UK. Therefore, within each country, the mean PCS and MCS scores remained stable over the study period.
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Discussion |
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Over the 4-yr study, there was greater patient attrition in the US which we adjusted for using imputations. Although we have shown that those patients who withdrew, were lost to follow-up, or otherwise provided incomplete data (excluding the deceased) did not appear to differ from those completing at least three SF-36 questionnaires, it is still possible that unobserved differences may remain. Such unobserved differences may also exist between deceased patients and other participants in addition to the differences observed at baseline. However, through a sensitivity analysis, we found that such potential differences of up to 50% in either direction have no impact on the quality of life outcomes (data not shown).
Although our patients may not necessarily be representative of all SLE patients, they were recruited from several centres and are likely more diverse than those described in prior quality of life studies. Given our patients had an average disease duration of 10 years, it is probable they had relatively stable disease and had adapted to their illness, consequently reporting a better quality of life than patients with disease for a shorter duration. However, by enrolling patients from specialized lupus clinics in tertiary centres, we may have included a greater proportion of patients with more severe disease than if we had recruited primarily from general rheumatology clinics.
The SF-36 assesses the preceding one-month period yet our patients were only surveyed yearly. Therefore, our longitudinal assessment of quality of life in SLE is not comprehensive. Given the rapidly fluctuating course of the disease, the instrument may not have captured the patient's full experience with SLE throughout the entire year. Fortin et al. [9] administered the SF-36 monthly and were able to demonstrate that over 6 months, the SF-36 scores changed with disease activity. Likely, administration of the SF-36 monthly or every three months would provide a more complete assessment of quality of life. Furthermore, the SF-36 is generic and thus may not be sufficient to characterize the numerous dimensions in which SLE may affect a patient (i.e. infertility, physical appearance). An SLE specific measure may be more appropriate and potentially should be incorporated in quality of life assessments [30, 31].
The compromised quality of life of patients with SLE becomes even more apparent when their PCS and MCS scores are compared to those in the general population. In our study, Canadian patients had mean baseline PCS and MCS scores of 40.6 and 46.0, respectively; American patients had mean scores of 37.4 and 45.0; and British patients had mean scores of 36.6 and 43.4. In the general population, Canadians of a similar age and sex as our study participants (female, age 3544), would be expected to have a mean PCS score of 51.5 and a mean MCS score of 50.2 [32]. In the US, the mean scores are 51.4 and 48.8 [18]; in the UK the mean scores are 52.4 and 48.3 [33]. As expected, chronic illnesses other than SLE have been shown to impact negatively on the SF-36 scores. For example, in the US, people with arthritis have mean PCS and MCS scores of 43.2 and 48.8; people with congestive heart failure have mean scores of 31.0 and 45.7; and people with diabetes have mean scores of 39.0 and 47.9 [18].
In summary, this 4-yr longitudinal study showed that quality of life remains stable over time in patients with SLE across countries that differ in their health care expenditure.
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Acknowledgments |
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The authors thank all the participating physicians (University of Birmingham: Margaret Allen, Simon Bowman), Jennifer Gardner for her expert technical assistance, and the patients whose contribution made this study possible.
Supported by grants from the Fonds de la recherche en santé du Québec and The Arthritis Society of Canada. The Montreal General Lupus Cohort is partially supported by the Singer Family Fund for Lupus Research; the Hopkins Lupus Cohort is supported by National Institutes of Health (NIH) RO1 AR43727 and by the Outpatient Clinical Research Center, RR 00722; the Pittsburgh Cohort is supported by the Lupus Foundation, Pennsylvania Chapter, K24 AR00213, NIH RO1 AR46588, Arthritis Foundation, National, NIH/5RO1 AL54900-02; the Birmingham Cohort is supported in part by the Wellcome Trust Clinical Research Facility and Lupus UK. A.E.C. is an Investigator of the Canadian Institutes for Health Research; M.P. is supported by the Hopkins Lupus Cohort RO1 AR43727-06 and the General Clinical Research Center MO1-RR 00052; L.J. is a Senior Investigator of the Canadian Institutes for Health Research; P.R.F. is an Investigator of The Arthritis Society and the Director of Clinical Research at the Arthritis Centre of Excellence.
The authors have declared no conflicts of interest.
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References |
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