The systemic lupus erythematosus Tri-nation Study: absence of a link between health resource use and health outcome

A. E. Clarke1,2, M. Petri3, S. Manzi4, D. A. Isenberg5, C. Gordon6, J.-L. Senécal7, J. Penrod2,8, L. Joseph2,9, Y. St Pierre2, P. R. Fortin10, N. Sutcliffe5, J. Richard Goulet7, D. Choquette7, T. Grodzicky7 and J. M. Esdaile11 for the Tri-nation Study Group{dagger}

1 Divisions of Clinical Immunology and Allergy and 2 Clinical Epidemiology, Department of Medicine McGill University Health Centre and Departments of 8 Economics and 9 Epidemiology and Biostatistics, McGill University, Montréal, Québec, Canada, 3 Department of Medicine (Division of Rheumatology), Johns Hopkins University School of Medicine, Baltimore, MD, 4 Department of Medicine (Division of Rheumatology) and Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA, 5 Centre for Rheumatology, Department of Medicine, University College London, London, 6 Department of Rheumatology, University of Birmingham, Birmingham, UK, 7 Department of Medicine (Division of Rheumatology), Hôpital Notre-Dame, Université de Montréal, Montréal, Québec, 10 Department of Medicine (Division of Rheumatology) and Department of Epidemiology, University of Toronto, Toronto, Ontario and 11 Arthritis Research Centre of Canada and Department of Medicine, University of British Columbia, Vancouver, BC, Canada.

Correspondence to: A. Clarke, Division of Clinical Epidemiology, Room L10-413, Montreal General Hospital, 1650 Cedar Avenue, Montreal, Quebec H3G 1A4, Canada. E-mail: ann.clarke{at}mcgill.ca


    Abstract
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Objective. Health consumption and health status in SLE in three countries with different health funding structures were compared.

Methods. Seven hundred and fifteen SLE patients (Canada 231, USA 269, UK 215) were surveyed semi-annually over 4 yr for health resource utilization and health status. Cross-country comparisons of (i) cumulative health expenditure (calculated by applying 2002 Canadian prices to resources in all countries) and (ii) disease damage (Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index, SLICC/ACR DI) at study conclusion were performed after adjustment. Missing expenditure and damage data were managed through multiple imputation using best predictive regressions with all available data from all patients as potential covariates.

Results. Four hundred and eighty-five patients provided data at study entry and conclusion and at least four resource questionnaires (Canada 162, USA 157, UK 166); 41 died (Canada 13, USA 18, UK 10); 189 withdrew, were lost to follow-up or provided data at entry and conclusion but fewer than four resource questionnaires (Canada 56, USA 94, UK 39). At conclusion, after imputation, in Canada, the USA and the UK respectively, mean cumulative costs per patient over 4 yr [95% confidence interval (CI)] were $15 845 (13 509, 18 182), $20 244 (17 764, 22 724) and $17 647 (15 557, 19 737) and mean changes in SLICC/ACR DI were 0.49 (0.39, 0.60), 0.63 (0.52, 0.74) and 0.48 (0.39, 0.57). After adjustment for baseline differences, on average (95% CI), Canadian and British patients utilized 20% (8%, 32%) and 13% (1%, 24%) less resources than patients in the USA respectively, but experienced similar health outcomes.

Conclusion. Despite patients in the USA incurring higher health expenditures, they did not experience superior health outcomes.

KEY WORDS: Systemic lupus erythematosus, Economics, Disease damage, SLICC damage index, Direct health care costs


    Introduction
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Cross-country comparisons of health-care expenditures and health outcomes can inform policy makers and providers about the relative efficiency of the respective health-care systems. Such comparisons help determine if patterns of care in a particular country are cost-minimizing, i.e. achieve the same health outcome at lower costs. Unlike cross-country comparisons conducted at the population level, which are only able to examine crude indicators of health outcome, we conducted a clinic based cross-country comparison for a specific condition, i.e. systemic lupus erythematosus (SLE), which can provide information on more precise outcomes and adjust for between-country differences in factors integral to health production and maintenance.

The countries which we have studied differ in their sources, mechanisms and amount of health-care financing. Health-care in Canada and the UK is primarily publicly funded, whereas in the USA it is primarily privately funded. In 2000, public expenditure on health, as a percentage of total expenditure on health, in Canada, the USA and the UK was 72, 44.3 and 81% respectively [1]. In the USA, private health insurance is supported, in part, by tax breaks referred to as ‘tax expenditure subsidies’. If these subsidies are included with public health expenditure, it has been estimated that the public share of total health expenditure approaches 60% [2].

The per capita total expenditure on health, expressed in US$ purchasing power parity, in Canada, the USA and the UK was $2269, $4631 and $1763 respectively, representing 9.1, 13 and 7.3% of the nation's gross domestic product [1]. Purchasing power parity is a type of currency conversion reflecting the currency required to purchase a given basket of goods in different countries. Differences in expenditures converted to a common currency at these rates would then represent only differences in the volume of goods and services purchased rather than short-run fluctuations in market exchange rates. However, purchasing power parities are an imperfect method of eliminating the price differential for medical goods and services as they consider a global basket of goods and services and not specifically medical resources. Despite this, it is still likely that the between-country expenditure differences reflect differences in utilization rates.

Despite considerably greater per capita expenditure on health in the USA, crude indicators of the overall health of the population do not reveal a more favourable profile of health indicators in the USA. For example, in 1999, the life expectancy of males at birth in Canada, the USA and the UK was 76.3, 73.9 and 75 yr [1] respectively; infant mortality, expressed as deaths per 1000 live births, in Canada, the USA and the UK was 5.3, 7.1 and 5.8 respectively [1]. However, such broad measures provide little information regarding the overall quality of health care as they fail to incorporate the between-country heterogeneity in factors such as ethnicity and socioeconomic status, which are important determinants of the production and maintenance of good health.

The performance of health-care systems can also be assessed in terms of disease categories at a population level. In comparing the potential years of life lost due to respiratory, cerebrovascular and ischaemic heart diseases between Canada, the USA and the UK, Canada performs most favourably and the UK the least; for neoplastic disease, there is little between-country difference [1]. Rather than examining broad disease categories which aggregate heterogeneous conditions to explore a potential causal link between health expenditure and health outcome, it should be more informative to consider specific disease entities. For example, following a myocardial infarction, patients in the USA are much more likely than their Canadian counterparts to undergo revascularization procedures and may experience an improved quality, and possibly quantity, of life [3]. It has also been shown, in an observational study involving a single Canadian and American centre, that American patients with hip or knee osteoarthritis undergo joint replacement earlier in their disease course and achieve greater postoperative function [4]. In contrast, when cancer survival was compared between residents of low-income metropolitan areas of Canada and the USA, Canadians experienced a survival advantage for most cancer sites, suggesting that the more equitable access to preventive and therapeutic health services in Canada may be responsible for the difference [5].

We have conducted the first transnational comparison of resource utilization and health outcome among patients with a chronic rheumatological condition managed primarily by non-invasive means. We have previously published a cross-sectional comparative analysis of the study entry data [6] and have reported that, after adjustment, Canadian patients reported better well-being than their American and British counterparts. Overall resource utilization did not vary substantially between the countries, although there was a trend towards more intense use of inpatient services in Canada and outpatient services in the USA. We have now concluded the longitudinal data collection over 4 yr. This paper presents the cross-country comparisons of cumulative health-care expenditure and health outcome, expressed as SLE disease damage, at study conclusion.


    Patients and methods
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Patients
The study design is described in detail elsewhere [6]. In brief, consecutive patients presenting to physicians’ offices in each of six tertiary care centres and fulfilling at least four of the American College of Rheumatology (ACR) revised criteria for SLE [7, 8] were invited to participate in a comparative study on health resource utilization and health status. There were two centres in each of three countries: the Montreal General Hospital and Hôpital Notre-Dame, Montreal in Canada; Johns Hopkins University School of Medicine, Baltimore and the University of Pittsburgh in the USA; and University College Hospital, London and the Queen Elizabeth Hospital, Birmingham in the UK. Patients were enrolled from June 1995 through to February 1998. Approval from the Institutional Review Board at each centre was obtained prior to study commencement. Informed consent was received from each participant.

Procedures
At study entry, patients reported on age, sex, ethnicity, education level and marital status. Disease duration represented the interval between disease diagnosis and study entry. Disease diagnosis was defined as the date the patient first met four ACR diagnostic criteria [7, 8]. At study entry and annually, for a maximum of 4 yr, participants completed questionnaires on health status, social support and satisfaction with health-care. At study entry and semi-annually, also for a maximum of 4 yr, they completed questionnaires on health resource utilization. Patients were assessed by their treating physician at study entry and study conclusion and disease activity and damage measures were completed by their physician.

Study instruments
Health status was assessed by the Medical Outcomes Survey Short Form 36TM (SF-36TM) [9, 10] and a visual analogue scale (VAS) adapted from the EuroQol [11, 12]. The SF-36TM includes eight dimensions of well-being: physical functioning, role limitations due to physical problems, bodily pain, general health perceptions, vitality, social functioning, role limitations due to emotional problems, and mental health. Physical and mental component summary scores were also calculated [13].

Social support was evaluated through the Interpersonal Support Evaluation List (ISEL) [14] which evaluates four dimensions of social support: tangible (availability of material aid), belonging (availability of people one can do things with), appraisal (availability of people to confide in), and self-esteem (availability of a role model).

Patients indicated their level of satisfaction with their medical care using the Medical Outcomes Study Patient Satisfaction Questionnaire (version IV) [15], which enquires about their global level of satisfaction considering all health-care providers and settings. It characterizes seven dimensions of satisfaction with care: general satisfaction, technical competence (i.e. diagnosis and management), interpersonal manner (e.g. courtesy and respect), communication, time spent with doctor, financial aspects, and accessibility and convenience of care. For our purposes, the financial subscale was omitted because it poses questions of limited applicability to Canadian and British patients due to the primarily public funding of health care in these two countries.

Health resource utilization was evaluated with a modified version of the economic portion of the Stanford Health Assessment Questionnaire [16], which has been validated by ourselves [17] and others [18] for SLE and other rheumatic diseases. It enquires about the utilization of all health services over the preceding 6 months, without asking the respondent to make attributions to SLE or any other conditions. Patients reported on outpatient utilization of physicians, non-physician health-care professionals, laboratory tests, imaging procedures, renal dialysis, prescription and non-prescription medications, assistive devices (e.g. walking cane), emergency rooms, ambulances and outpatient surgery as well as stays in acute and non-acute care facilities. The translation of health services utilization data into direct health costs is detailed in the next section.

Disease activity was assessed by the treating physician using the revised Systemic Lupus Activity Measure (SLAM-R) [19], as well as with a VAS of current activity and activity over the past year. Disease damage was evaluated using the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SLICC/ACR DI) [20, 21], which incorporates cumulative and irreversible involvement due to SLE, its treatments, or intercurrent illnesses.

Calculating costs
To compare the overall value of resource utilization across countries, it is necessary to collapse diverse resource components into a single measure by assigning costs to these resources. Given that the cost of resources is the product of their quantity and price, any cross-country differences in cost could be secondary to either differences in quantity and/or price of resources. Since our primary comparison is resource consumption, it is necessary to apply a constant price across countries for each health service. Any observed cost differences can then be attributed to differences in pattern or frequency of resource utilization. Canadian prices (2002 dollars) were applied to each health service across all three countries, providing the measure of expenditure and/or resource utilization that will be used throughout this paper. The method for calculating direct [6] costs has been published by us previously. By multiplying 2002 Canadian costs by 0.84 and 0.53, they can be converted into 2002 US dollars and 2002 UK pounds respectively (www.OECD.org). This conversion factor does not provide the actual costs of treating patients in the USA or in the UK. It would only provide the actual cost under the hypothesis that the prices for medical services would be the same in all three countries, which is unlikely to be the case.

In brief, using a costing method employed by ourselves [6, 17, 22] and others [18, 23, 24], costs were assigned to each unit of service as follows. Canadian national estimates were used whenever available. When such Canadian-wide data did not exist, prices were developed by averaging data from a province with relatively low per capita expenditure on health-care, Quebec, and a province with relatively high per capita expenditure, Ontario [25]. Canadian prices were chosen because, unlike the USA, each province has a single payer for most health services.

The prices for physician services, the professional and technical component of laboratory and imaging tests, and renal dialysis were assigned according to the provincial government reimbursement schedule. For non-physician services, costs were obtained from provincial professional associations. Estimates of prescription and non-prescription medications costs were calculated as the product of the weighted average cost per milligram (inclusive of dispensing fees), total daily dose, and therapy duration with all cost data obtained from Intercontinental Medical Statistics. Prices for assistive devices were obtained from local medical suppliers and provincial health insurance schedules. Emergency room visit costs were estimated by incorporating data from professional reimbursement schedules, diagnostic and therapeutic cost per visit as published by Statistics Canada [26], and the ratio of diagnostic and therapeutic costs to overall hospital operating costs. The cost of acute hospital stays and outpatient surgeries were calculated according to the method of the Canadian Institutes for Health Information [27–29], whereby a per diem cost is adjusted by a factor which incorporates the intensity of resources used based on the reason for hospitalization or type of outpatient surgery performed. The cost of non-acute care facilities was based on Statistics Canada data on per diem costs [30].

Statistical methods
Data on demographics, health status, social support, satisfaction, direct costs and disease characteristics were summarized across countries using means and standard deviations (S.D.) as well as medians, interquartile ranges and proportions where appropriate.

Cross-country comparisons of (i) cumulative health-care expenditure (expressed in 2002 Canadian dollars) and (ii) SLE disease damage (SLICC/ACR DI) at study conclusion were performed. Seemingly unrelated regressions [31] were used for these two outcomes with indicator variables for the countries where the patient was receiving care with the USA as the reference country. These are regression equations that are related only through the correlation of their error terms, accounting for which can help minimize the variance around the estimated coefficients. Study entry values of age, sex, ethnicity (Caucasian vs non-Caucasian), education (both as years and categorical as <12 or ≥12 yr), marital status (married vs unmarried), disease duration, health status (individual SF-36TM subscales and 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 were used as potential covariates.

Missing semi-annual expenditure data and final damage data were managed by means of a multiple imputation strategy using best predictive regression models with all available data (measured at any time during the study) from all patients as potential covariates [32]. Briefly, in multiple imputation, regression models are created for each missing data item that predict the missing data based on all other items. Using these models, a complete data set is created that is used for analysis. This procedure is repeated many times, resulting in many different ‘complete’ data sets, and final results are averaged across these sets. Provided the data are missing at random (essentially meaning that they are reasonably well predicted by the non-missing items [32]), this method allows the regression analyses for cost and damage to include all subjects, thereby adjusting for any bias due to non-response and including all uncertainty inherent to the problem, including that due to unknown missing data [32]. ‘All subjects’ refers to those who died, withdrew, were lost to follow up, or provided data at study entry and conclusion but failed to complete all resource questionnaires.

For patients who died during the study, imputations were performed up to 4 yr after study entry despite the fact that death is obviously the end of disease progression and cost accumulation. This is because alternative strategies, such as excluding all deceased patients or including them without performing imputations, would either create a selection bias or make it appear as if death were cost-saving. Therefore, we chose to predict cross-country differences over a four year survival period, albeit only hypothetical for the minority of patients who died during the course of the study.

For the expenditure outcome, a logarithmic transformation was performed, given that the error terms from linear regressions for such dependent variables are often not normally distributed [33]. For all regressions, model selection was based on Bayes’ factor as approximated by the Bayesian Information Criteria (BIC) [34]. Models selected by the BIC have been shown to have better prediction properties on average compared with other model selection algorithms, such as backward or forward stepwise procedures.

The above multiple imputation analyses assume that the information available on those who died, withdrew or were lost to follow-up is sufficient to accurately predict their cumulative costs and damage accumulation (i.e. ignorable non-response) [32]. A sensitivity analysis was therefore done that was similar to the method of Kmetic et al. [35] to account for the possibility of non-ignorable differences between those who died, withdrew or were lost to follow-up and the remaining study participants. Alternative assumptions for these sensitivity analyses included: (1) multiplying by a factor of 2 both the imputed cumulative costs and the imputed final SLICC/ACR DI after the last available period of data for those who died or withdrew or were lost to follow up; (2) same as assumption 1, but only for those who died; and (3) the same as assumption 2 and multiplying imputed cumulative costs and imputed final SLICC/ACR DI by a factor of 0.5 instead of 2 for those who withdrew or were lost to follow-up. For comparison purposes, results are also presented without performing any imputations.


    Results
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Seven hundred and fifteen patients were enrolled in the study (Canada 231; USA 269; UK 215) (Table 1). Demographic data, health status, annual direct medical costs and disease features at baseline for all participants are presented in Table 2.


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TABLE 1. Participants in the Tri-nation Study

 

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TABLE 2. Baseline characteristics of study participants

 
The baseline characteristics of those who provided data at study entry and conclusion and completed a minimum of four resource questionnaires were compared with the baseline characteristics of those who died and those who withdrew, were lost to follow-up or provided data at entry and conclusion and fewer than four resource questionnaires. Table 3 presents, for each country, selected baseline characteristics of those providing data at entry and conclusion and at least four resource questionnaires (column 1) and the difference between these patients and those who died (column 2) and those who withdrew, were lost to follow-up or provided data at entry and conclusion and less than four resource questionnaires (column 3). In each country (Table 3, panels A–C, column 3), there were no clinical or statistically significant differences between those who completed at least four resource questionnaires and those who withdrew, were lost to follow up or provided data at entry and conclusion and less than four resource questionnaires. However, in all three countries, those patients completing at least four resource questionnaires differed from deceased patients (Table 3, panels A–C, column 2), the differences being greatest in Canada. Deceased Canadian patients were older than those providing at least four resource questionnaires [mean difference, 95% confidence interval (CI) was 13.2 (6.2, 20.1)] and more costly [$7415 (–96, 14 925)], with more disease activity [6.4 (3.8, 9.0)] and disease damage [4.5 (2.0, 7.0)]. A similar trend existed for British and American patients for age, cost and damage, but was less pronounced.


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TABLE 3. Baseline characteristics of patients who provided data at entry and conclusion and at least four resource questionnaires vs patients who died, withdrew, were lost to follow-up or provided data at entry and conclusion and less than four resource questionnaires

 
The cumulative costs for the individual resource components are shown in Table 4. These were calculated by weighting the number of 6-month observations contributed per patient. Difference patterns of health resource consumption were observed between the countries, British patients seeing fewer specialists and more non-specialist physicians, Americans using more laboratory and imaging procedures, and Canadians spending slightly less on medications.


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TABLE 4. Components of direct medical costsa

 
When all patients were included by using multiple imputations for those who were deceased, withdrew, were lost to follow up, or provided data at entry and conclusion but failed to complete all resource questionnaires, the mean (95% CI) cumulative direct medical costs were 15 845 (13 509, 18 182), 20 244 (17 764, 22 724) and 17 647 (15 557, 19 737) respectively (Table 5). If the costs imputed after death for those who died are excluded, the mean costs (95% CI) for Canada, the USA and the UK are $14 407 (12 178, 16 635), $18 970 (16 604, 21 335) and $16 779 (14 715, 18 843) respectively (data not shown). Although these imputed costs differ slightly from those in Table 4, the order of the level of costs across countries remains unchanged.


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TABLE 5. Patient characteristics at final study visita

 
The mean changes (95% CI) in the SLICC/ACR DI between study conclusion and study entry in Canada, the USA and the UK were 0.49 (0.39, 0.60), 0.63 (0.52, 0.74) and 0.48 (0.39, 0.57) (Table 5). If we consider the mean changes (95% CI) in the SLICC/ACR DI for only those patients providing data at study entry and conclusion, they are 0.45 (0.31, 0.59), 0.65 (0.50, 0.80) and 0.44 (0.33, 0.55), for Canada, the USA and the UK respectively (data not shown). As for costs, the order of level of change in damage does not vary across countries when comparing imputed and actual data.

The regression models for the outcomes of cumulative costs and SLICC/ACR DI at the final study visit are shown in Tables 6 and 7. In the regression model for cumulative costs, the country coefficient represents the percent change in cumulative costs relative to the reference country, i.e. the USA. Therefore, on average (95% CI), Canadians had 20% (8%, 32%) lower costs than Americans at study conclusion and the British had 13% (1%, 24%) lower costs than the Americans. In the regression model for the SLICC/ACR DI, the country coefficient represents the unit change in the SLICC/ACR DI relative to the reference country. Therefore, on average (95% CI), SLICC/ACR DI scores increased by 0.10 (–0.03, 0.23) units less in Canadians and by 0.12 (–0.01, 0.26) units less in the British relative to the Americans at study conclusion. The regression coefficients for damage overlap zero, but also do not exclude the possibility that patients in Canada and the UK experience clinically superior health outcomes. After adjustment, we have thus shown that SLE patients in Canada and the UK utilized fewer health-care resources than those in the USA, but experienced similar health outcomes. However, the width of the confidence interval for the damage scores does not exclude potentially important between-country differences.


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TABLE 6. Regression model for cumulative expenditure

 

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TABLE 7. Regression model for final SLICC/ACR DI

 
Most of the alternative assumptions made in the sensitivity analyses produced no significantly different results in the country coefficients for the outcomes of cumulative expenditure and damage. In the regression models for cumulative expenditure, the coefficients (95% CI) for Canada were –0.23 (–0.37, –0.10), –0.24 (–0.37, –0.11) and –0.22 (–0.36, –0.09) for assumptions 1–3 respectively; for the UK, they were –0.19 (–0.32, –0.06), –0.14 (–0.26, –0.01) and –0.09 (–0.22, 0.04) respectively. For the final SLICC/ACR DI, the regression coefficients (95% CI) for Canada were –0.03 (–0.37, 0.31), –0.14 (–0.43, 0.15) and –0.16 (–0.51, 0.18) for assumptions 1–3 respectively; for the UK, they were –0.10 (–0.43, 0.23), –0.06 (–0.34, 0.23), and –0.05 (–0.39, 0.28) respectively. It should be noted, however, that in the regression model for cumulative expenditure, under assumption 3, where the imputed costs and final SLICC/ACR DI are multiplied by 2 for those who died and by 0.5 for those who withdrew or were lost to follow-up after the last available period of data, British patients no longer incurred significantly lower costs than Americans, whereas Canadian patients continued to do so.

The regression models presented in Tables 6 and 7 can also be used to predict long-term health-care expenditure and disease damage. As an illustrative example of the interpretation of these regression models, each 10-unit decrease in the SF-36TM physical function scale at baseline implies a 5% increase in cumulative costs and a 0.05-unit increase in the SLICC/ACR DI at study conclusion; each 1-unit increase in the SLICC/ACR DI at baseline implies a 7% increase in cumulative costs and a 1.07-unit increase in the SLICC/ACR DI at study conclusion.


    Discussion
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
We have performed the first transnational comparison of health resource utilization and health outcome among patients with a chronic rheumatological condition managed primarily by non-invasive means. In contrast to transnational comparisons conducted at the population level on the generally healthy or on broad disease categories, we evaluated a discrete condition, SLE, allowing us to concentrate on more informative indicators of health outcome and adjust for between-country differences in factors potentially influencing outcome. We showed that although SLE patients in Canada and the UK incurred lower health-care expenditures than those in the USA, there were no differences in health outcomes.

We observed that in all countries deceased patients relative to all other participants tended to be older, with higher expenditure and more disease damage at baseline, the differential being greatest in Canada. Through our imputations and regression analyses, we demonstrated that it was not because of the death of the sickest, most costly patients during the course of the study that Canadians incurred lower health-care expenditure. The multiple imputation strategy assumes that the information available on those who died, withdrew or were lost to follow-up is sufficient to accurately predict their cumulative costs and damage accumulation (i.e. it assumes that the association between the covariates and the outcomes for those providing data at study entry and conclusion is the same as the association between these variables for the remaining participants). However, if this relationship is not sufficiently similar to be considered as ignorable non-response, we also conducted a sensitivity analysis. In this analysis, where we multiplied by 2 the imputed cumulative costs and final SLICC/ACR DI after the last available period of data for all those who died (i.e. assumption 2), we showed that the country coefficients do not change substantially.

The proportion of patients who withdrew or were lost to follow-up was greater in the USA. This was probably due, in part, to patients changing insurance status and being unable to continue to receive care from the participating site. Although we observed little difference in baseline characteristics between these patients and those who completed a minimum of four resource questionnaires, it is still possible that they were less ill and that the higher attrition of such potentially less sick patients in the USA could inflate the American costs. Assuming that this difference between those who withdrew or were lost to follow-up and remaining participants is ignorable non-response, then our imputations accurately predict the cumulative costs and damage for the former participants. If the difference was non-ignorable, we conducted a sensitivity analysis where we multiplied by 0.5 the imputed costs and damage scores after the last available period of data for those who withdrew or were lost to follow-up, and the excess cumulative expenditure in the USA relative to Canada remained. Under this assumption, the USA is no longer significantly more costly than the UK. However, we believe that this assumption is probably unrealistic as our baseline data do not reveal a difference between those who withdrew or were lost to follow-up and those completing at least four resource questionnaires.

Although it would have been informative to consider health outcomes other than the SLICC/ACR DI, such as the SLAM-R and SF-36TM, these latter two instruments enquire about the respondent's experience over the preceding month. Hence, in our study, they were administered too infrequently to provide a comprehensive reflection of the patient's potentially fluctuating status over the entire 4-yr study interval. In contrast, the SLICC/ACR DI either remains unchanged or increases with time and therefore, in our study, is a better representation of the overall change in a patient's status.

The cross-country differences in health resource expenditure that we observed can potentially be ascribed to a variety of interrelated factors: different systems of health-care funding, other country-specific determinants, and individual physician and institutional characteristics. Our study does not allow us to disentangle the contribution of each of these influences to the between-country differences. If the differences were primarily due to physician or institutional characteristics independent of funding structure, then the within-country difference would be expected to exceed the between-country difference. However, in our study, the cumulative expenditure and damage accumulation are similar across the two centres within each country, suggesting that the structure of health-care funding is at least partially responsible for the country effect. Although an alternative study design involving national administrative claims databases would enable us to evaluate a more heterogeneous population from more treatment centres, the data would not contain information on SLE disease activity and damage and other relevant descriptors of health status, thereby limiting our ability to evaluate a link between expenditure and outcome. In future work, it would be of interest to include a more representative population from each country that can provide detailed data on both resource utilization and relevant health outcomes. Such a design would strengthen the attribution of any observed between-country differences to differences in national health funding schemes. Furthermore, it would allow the relationship between factors such as ethnicity and socio-economic and health insurance status, which have been reported to influence both utilization and outcome [36–39], to be more fully examined.

It is possible that costs were higher in the Americans because we observed insured patients. At study entry, 73% of the American participants reported having private medical insurance and 25% had public insurance. However, regardless of why the American patients incurred higher costs, if health expenditure were positively related to health outcome, we would anticipate that these higher costs would be associated with superior outcomes, but this was not observed.

Few such cross-county comparative studies have been conducted for musculoskeletal conditions [4], and none for SLE. However, within the USA, the utilization and outcome for patients with musculoskeletal conditions enrolled in different health-care plans has been compared and little difference has been found [40–43]. It has been shown that outcome in rheumatoid arthritis is positively associated with the amount of contact with a rheumatologist [41, 44, 45], leading to speculation that the lack of difference between health plans may be due to the involvement of rheumatologists in both; outcome may be much more dependent on the type of treating physician than on the type of reimbursement. Similar to rheumatoid arthritis, it has been shown that contact with a physician experienced in lupus management was positively associated with a favourable outcome, longer times before referral to a rheumatologist increasing the probability for renal insufficiency [46] and hospitals with higher volumes of lupus patients reporting lower mortalities [47]. In our own study, it is possible that cross-county differences in utilization and outcome may have been attenuated because all patients received care from a lupus specialist and utilization and outcome may be more dependent on whether a rheumatologist/immunologist was involved as the treating physician than on the country where the care was delivered.

In conclusion, in the first cross-county comparative study on SLE, we have shown that despite significantly greater health resource utilization in the two American sites, these patients do not experience superior health outcomes. This could potentially suggest that Canada and the UK, relative to the USA, may practise a pattern of care which is cost-minimizing, i.e. they achieve equivalent or superior outcomes at lower costs. However, before speculating on the possible implications of our findings for health policy, further research is required to confirm our observations and to better elucidate the factors mediating between-country differences.


    Acknowledgments
 
The authors thank all the participating physicians (University of Birmingham: M. Allen, S. Bowman), E. Toto and J. Gardner for their expert technical assistance, and the patients whose contribution made this study possible. This work was 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 RO1 AR43727 and by the Outpatient Clinical Research Center, RR 00722; the Pittsburgh Cohort is supported by the Lupus Foundation, Pennsylvania Chapter, K24 AR00213, and 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 RO1 AR43727-06 and RR00052; 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.


    Notes
 
{dagger}Tri-nation staff: Montreal General Hospital: T. Panaritis, P. Panaritis, K. Margonis, M. Trifero, D. Ferland, C. Neville, M. Orsini-Dudin; University of Pittsburgh: J. Rairie; University of Birmingham: S. Heaton. Back


    References
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 

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Submitted 4 February 2004; revised version accepted 8 April 2004.