Case Study The Economics Of End-Stage Renal Disease

Background: As the incidence of end-stage renal disease (ESRD) is on the rise, new therapies are being developed for delaying ESRD. This study is aimed at constructing a generic model for estimating the cost-effectiveness of delaying ESRD in 7 European countries: the Netherlands, United Kingdom, Germany, Italy, Spain, Finland and Hungary. The use of this model is illustrated by assessing 2 fictitious, but realistic therapy options. Methods: Quality-adjusted life years (QALYs) and societal costs were estimated using a state-transition model. Age-dependent survival after renal replacement therapy was estimated using data from the Dutch Renal Registry. Healthcare costs and utilities were obtained from published reports. Country-specific differences regarding access to transplantation and value of productivity were factored. Results: A 1-year delay of ESRD rendered an estimated gain of 0.6 QALYs and 0.3 years in productivity. Access to transplantation had a minimal impact, whereas savings on productivity had a significant impact. For a 1-year delay free of charge, societal savings would range from €8,000 in the United Kingdom to €17,000 in Germany. Applying thresholds of €20,000-€40,000 per QALY gained, one-time cell-based therapy would be economically acceptable if it delayed ESRD by 0.2-0.5 years. It would be cost saving for a delay in excess of 0.5 years. Continuous use of medication is unlikely to be cost-effective for prices higher than €30,000 per year. Conclusion: This study provides evidence for the economic potential of new therapies delaying ESRD. The constructed model provides users with information about the market success rates of treatment options at an early stage.

© 2016 The Author(s) Published by S. Karger AG, Basel


Introduction

The worldwide impact of chronic kidney disease (CKD) is high. Following the growing prevalence of diabetes around the world, the incidence of CKD and end-stage renal disease (ESRD) is increasing [1]. In Europe, CKD affects approximately 8% of the population. To cope with the increase in CKD and ESRD incidences, governments spend a considerable part of their healthcare budget on expensive renal replacement therapy (RRT) [2]. In addition, the costs for patients, family and society as a whole are considerable, due to the impact on the quality of life, ability to work, life expectancy and the invasive character of RRT [3]. Currently, no treatment options are available for restoring the kidney function in patients with CKD. Conventional therapy includes basic pharmacological and life style interventions, which aims at delaying and reducing further damage [1]. New treatment methodologies for delaying ESRD, including, for example, cell-based therapy and biological agents are under development.

Traditionally, renal treatment has been one of the first to undergo economic assessment. The most quoted threshold for cost-effectiveness in healthcare is $50,000 per quality-adjusted life year (QALY), which implies that $50,000 is considered a reasonable price for one additional healthy year. This threshold was derived from the cost-effectiveness ratio calculated for using dialysis in patients with chronic renal failure. If $50,000 per QALY is acceptable for dialysis, then other interventions with similar or better cost-effectiveness could likewise be considered acceptable [4].

In this paper, a model for assessing the cost-effectiveness of new treatments that delay ESRD is presented. This model allows assessing the economic potential of new therapies in an early phase of their development, based on preliminary estimates of their costs and effectiveness. The model was developed for several European Union countries. The use of this model was illustrated by assessing 2 fictitious, but realistic therapy options: one-time cell-based therapy and a continuous use of biological agents.

Methods

Cost utility analysis (CUA) was performed for estimating the potential impact of delaying ESRD in 7 European Union countries. CUA investigates whether a new treatment is good value for money by explicitly comparing its impact on costs and patient effectiveness in terms of QALYs. The relevant population for analysis consisted of CKD stage-4 (CKD4) patients, aged over 20 years, from the following European countries: the Netherlands, United Kingdom, Germany, Italy, Spain, Finland and Hungary. The chosen countries represent a geographical spread across Europe, with considerable differences in transplantation rates and productivity values.

General Model

Figure 1 shows the 5 health states in the model: hypothetical patients enter the model in CKD4 and move forward through ESRD to death. A continuous-time state-transition model, with age-dependent fixed state durations, was used. The model was evaluated for different initial ages, and then weighed by age distribution.

Fig. 1

Health states of the state-transition model.

Treatment performance was characterized by the delay in disease progression towards ESRD, averaged over the population that the new treatment is applied to. This delay was modelled as a prolongation of the time spent in CKD4, during which new treatment is provided alongside the conventional therapy. In the subsequent ESRD stage, the model differentiates between dialysis and transplantation as RRT, since costs, utility and life expectancy are different for these treatment options. Categorization of RRT was based on the main therapy received. The dialysis option represents ESRD patients who receive dialysis and no transplantation, whereas the transplantation option represents patients who receive transplantation and may in addition also receive dialysis while on the waiting list or after losing the graft.

Costs are presented in euros at 2016 price level. Costs and QALYs were discounted at 3% per year, thus giving less weight to more distant years. The model was programmed in Microsoft Excel 2010 and is available as web content (www.lumc.nl/org/medische-besliskunde/medewerkers/904030438095212).

Survival Estimates

Since renal registries only provide information from the moment RRT starts, the clinical epidemiology of earlier CKD stages is poorly understood, particularly with regard to progression between stages. An estimated average duration of 2 years to progress from CKD4 to ESRD was based on previous studies [5,6,7]. Additional time spent in CKD4 due to the new intervention is input to the model and needs to be determined outside of the model, depending on specific parameters of the intervention under investigation.

To estimate the age distribution for RRT and survival after RRT, data from the Dutch Renal Registry (Nefrovisie/Renine: www.renine.nl), containing information of all Dutch individuals receiving RRT [8,was used]. Nefrovisie/Renine provides time-to-death data for both dialysis and transplantation patients aged over 20 years who started therapy from 1995 to 2005. During this period, 14,499 RRT patients were registered, of which 4,642 (32%) received renal transplantation. The median age at RRT initiation was 63 years. The age-dependent remaining life expectancy after RRT (fig. 2) was estimated using Weibull survival analysis, with shape and scale parameters quadratic in age [9]. The estimates were used for all countries in the analysis, assuming similar survival across countries.

Fig. 2

Age-dependent remaining life expectancy.

Distribution Over Dialysis and Transplantation

The probability of RRT patients receiving either dialysis or transplantation depends on national policies, waiting list issues and patients' age. The average transplantation probabilities by country were estimated from RRT incidence rates (table 1), which were in turn estimated from RRT prevalence data [10] combined with survival estimates. The relative transplantation probabilities by age were estimated from the Dutch age pattern (on an average 28%, ranging from 87% at 20 years to 0% above 75 years).

Table 1

Country-specific RRT rates and productivity values

Healthcare Costs

Costs of the new intervention are input to the model and need to be determined outside of the model, depending on specific parameters of the intervention under investigation. Other country-specific healthcare costs were obtained from published reports [11,12,13,14,15] (table 2). The costs of conventional therapy in the CKD4 health state also apply to the prolonged CKD4 phase. The annual costs of dialysis are a weighted average of haemodialysis and peritoneal dialysis when sufficient data were available [14,15]. For those countries and health states for which no cost data were found, the average health state costs over all other countries were used. All costs were converted to euros.

Table 2

Country-specific healthcare costs in 2016 euros

Utility Values

Utility for health-related quality of life reflects the value of health, on a scale anchored at 1 (perfect health) and 0 (as poor as death). Utility values (table 3) were obtained from published reports [3,14,16,17,18]. For comparing health states, only utility values generated by the same assessment method were considered. Since time-trade-off (TTO) was the only assessment method that had been applied to each of the health states, these TTO utilities were selected for the model.

Table 3

Utilities for the health states in the model

Productivity Value

The country-specific average annual productivity value among the general population was calculated using the values of labour participation [19], hours worked per week [19] and labour costs per hour [20] (table 1). Productivity was computed until pension at age 65 years.

The value of productivity among CKD patients was based on a Dutch study showing that 51% of pre-dialysis patients and 24% of dialysis patients participated in the labour market [21]. Compared to the general 75% Dutch labour participation, the relative productivity value for CKD4 was estimated at 68% (51/75) and for ESRD on dialysis at 32% (24/75). As utility after transplantation is very similar to utility in CKD4, the productivity after transplantation was considered to be identical to productivity among CKD4 patients. Societal costs were estimated using the human capital approach, by subtracting the full productivity value from healthcare costs.

Results

Figure 3 shows the patient benefits from delaying ESRD, by country and depending on the treatment performance. By delaying ESRD, the age at RRT commencement is increased and the remaining life expectancy after RRT somewhat decreased (fig. 2). As a result, the gain in life expectancy is about 90% of the duration of the delay. Countries with lower transplantation rates have slightly larger gains in life expectancy (92% for Germany, Hungary and Italy) than countries with higher transplantation rates (89% for the Netherlands and Finland).

Fig. 3

Average gain in patient outcome, depending on the delay of ESRD (ranked from top to bottom: (1) Germany, (2) Hungary, (3) Italy, (4) United Kingdom, (5) Spain, (6) The Netherlands, (7) Finland).

Considering the quality of life, a 1-year delay of ESRD results in an average gain of 0.6 QALYs (range 0.60-0.63). Bounded by the pension age of 65 years, the average gain in productivity was considerably lower at 0.3 years (range 0.28-0.35).

Healthcare and Societal Savings

Figure 4 shows the estimated healthcare and societal savings from delaying ESRD for the countries analysed, disregarding the costs of the new treatment. In the societal perspective, savings on productivity costs are included in addition to healthcare costs.

Fig. 4

Average life-long healthcare and societal savings, depending on the delay of ESRD.

Despite the increased life expectancy, the delay of ESRD results in net savings on healthcare. This is because, apart from the new treatment, the annual costs during the initial delay in CKD4 are relatively low, whereas the annual savings due to somewhat reduced life expectancy after RRT are high. The patterns for healthcare savings are similar in different countries, with longer delays leading to higher savings on RRT care. Countries with higher dialysis costs and lower transplantation rates have higher savings because prevented costs after RRT are higher for dialysis than for transplantation. For a 1-year delay of ESRD, savings on healthcare costs range from €2,000 in the United Kingdom to €10,000 in the Netherlands.

Savings on productivity originate from the gain in life expectancy prior to the pension age (fig. 3), and also from the higher productivity in CKD4 than when receiving dialysis. Country-specific differences in savings on productivity are substantial, due to the more than 4-fold difference in annual productivity value (table 1). For a 1-year delay of ESRD, savings on productivity range from €2,000 in Hungary (low on productivity value) to €10,000 in Germany (high on productivity value). The total societal savings range from €8,000 in the United Kingdom to €17,000 in Germany.

Costs-Per-QALY Analysis

Based on the expected delay of ESRD due to the new intervention, the model assesses the impact on life expectancy, QALYs and healthcare and productivity costs. Combined with the expected treatment costs, cost-effectiveness ratios are calculated from either the healthcare or the societal perspective. Figure 5 presents the range of estimated cost-per-QALY ratios, by selecting the delay towards ESRD on the horizontal axis and the treatment costs on the vertical axis. The dashed and solid lines represent the healthcare and societal perspectives, respectively. For example, from a societal perspective, a new treatment providing 4 years delay of ESRD and €100,000 treatment costs has a cost-utility ratio of about €20,000 per QALY. From a healthcare perspective, the cost-utility ratio is closer to €40,000 per QALY.

Fig. 5

Cost-effectiveness of delaying ESRD from the healthcare (dashed lines) and societal perspectives (solid lines), depending on the delay of ESRD and the total costs of the new treatment (averaged over the 7 countries). Case studies 1 and 2 are indicated.

The curves in the figure represent varying thresholds for how much one is willing to pay for better effectiveness. For example in the United Kingdom, the National Institute for Health and Care Excellence uses a threshold between £20,000 and £30,000 per QALY [22]. In the Netherlands, no formal threshold is used. Yet, similar thresholds of €20,000 or €40,000 per QALY are often used in discussions, or possibly €80,000 per QALY under specific conditions [23]. This approach will be illustrated by 2 fictitious, but realistic therapy options.

Case 1: One-Time Cell-Based Therapy

The STELLAR consortium (www.stellarproject.eu) focusses on using stem cells isolated from the kidney for delaying ESRD. Preliminary results obtained within the project suggest a reparative capacity of these stem cells. As this stem cell-based therapy is under development and has not yet been applied in clinical practice, its performance in patient benefit is still uncertain. According to expert opinions, it could be effective in 50% of the patients it is applied to (range 10-100%) and when effective, could delay ESRD by 2 years (range 1-5 years). Therefore, an average delay of 1 year in the analysis is assumed. In addition, the costs of one-time stem cell-based therapy from actual costs for the preparation of bone marrow mesenchymal stromal cells were estimated. It was also estimated that the therapy could be provided at a cost of €7,200, including isolation of material (€800), expansion and preparation (€6,000) and the infusion of stem cells in 2 hospital visits (€400). Costs would be higher when costs of facilities and overhead, intellectual property, or commercial profit margins are included. However, costs could also be lower due to economies of scale.

Figure 5 shows that for the estimated 1-year delay and €7,200 treatment costs, one-time stem cell-based therapy would be cost saving. Therefore, this treatment would be economically acceptable regardless of the specific threshold for acceptable costs. It would still be cost saving if the delay would turn out to be 0.5 years. Applying a threshold of €20,000 and €40,000 per QALY, one-time stem cell-based therapy would be economically acceptable if it delayed ESRD by 0.2-0.5 years.

Case 2: Continuous Medication Use of a Biological Agent

For continuous treatment, the treatment costs are incurred over the duration of the CKD4 period, including the delay period. Therefore, treatment costs increase with the delay. Consider a hypothetical drug that is initiated early in the CKD4 stage and that substantially delays ESRD by 10 years (thus extending the CKD4 period from 2 years to 12 years). Costs of the drug are €30,000 per year, with a value of €306,000 over 12 years (discounted at 3%). A 10-year delay is estimated to provide a gain in patient outcome of 5.6 QALYs (fig. 3), with savings of around €54,000 on healthcare costs and €95,000 on societal costs (fig. 4). Combined with the treatment costs, the healthcare and societal cost-utility ratios are estimated at €45,000 and €38,000 euro per QALY, respectively (fig. 5). According to health economic standards in many countries, this would be only just acceptable.

For shorter delays than 10 years, cost-effectiveness would be worse as the costs of the biological agent in the initial 2 years of the CKD4 period have a relatively large impact. For a delay of 1 year, the period of medication use would be 3 years, increasing the cost-utility ratios to more than €100,000 euro per QALY. Therefore, continuous medication use delaying ESRD is unlikely to be cost-effective for prices higher than €30,000 per year.

Discussion

In this study, a generic model for early assessment of the cost-effectiveness of delaying ESRD in several European Union countries was developed. The model applies to interventions that can be characterized by how successful they are in delaying the disease progression. In general, a 1-year delay of ESRD was estimated to improve the patient outcome by 0.9 life years and 0.6 QALYs and to improve productivity by 0.3 years. Country-specific differences in access to transplantation had minimal impact on patient outcome, but differences in costs were substantial: for a 1-year delay free of charge, savings on healthcare costs would range from €2,000 in the United Kingdom to €10,000 in the Netherlands and societal savings would range from €8,000 in the United Kingdom to €17,000 in Germany.

This study is the first of its kind to model the generic delay of ESRD for the purpose of early assessment of therapies under development. Studies performing cost-effectiveness analyses for a similar patient population generally compare 2 specific therapies in ESRD [24,25] or in CKD [7,26]. Another approach compares several treatment modalities within one model [27]. The current model is a flexible model that allows for varying country parameters and preliminary treatment effectiveness and cost estimates. In doing so, a model suitable for the early assessment of any treatment aiming at slowing the progression to ESRD was developed successfully.

Early detection and treatment of disease is often beneficial. This is also true with respect to delaying ESRD: prolonging the CKD4 stage improves productivity and delaying RRT reduces healthcare costs. Depending on the costs of the new intervention, effective treatment can combine improved patient outcomes with savings to society. Two specific cases were analysed using this model, applying acceptability thresholds of €20,000-€40,000 per QALY gained. The analysis showed that one-time cell-based therapy with €7,200 treatment costs would be economically acceptable if it delayed ESRD by 0.2-0.5 years. It would be cost saving for a delay in excess of 0.5 years. Alternatively, continuous use of a biological delaying ESRD is unlikely to be cost-effective for prices higher than €30,000 per year. These case studies illustrate 2 different approaches. The first case starts from a cost-price analysis and shows whether the intervention would be considered cost-effective at those costs. In the second case study, no cost-price analysis was performed. Instead, the analysis showed the maximum reimbursable price of the new treatment. Such a ‘headroom analysis' can provide users details on whether the intervention has a realistic chance of market success.

One of the important mechanisms in the model is how age determines the outcome after RRT. Figure 2 shows that the survival for dialysis is relatively poor. Moreover, it shows a relatively flat curve, indicating that survival after dialysis is primarily determined by disease progression and not by age. As a result, a delay in disease progression almost completely translates into longer survival. The curve for life expectancy after transplantation is steeper, so a larger part of the initial delay is counteracted by reduced survival after transplantation. Thus, in general, delaying dialysis is more cost-effective than delaying transplantation. Therefore, the survival difference between dialysis and transplantation explains why the impact on patient outcomes is more favourable in countries with lower transplantation rates. However, these differences are small.

This study has several limitations. First, early estimates of the effectiveness and costs of a new intervention are uncertain and tend to be too optimistic. Second, the quality of a model can only be as good as the quality of the data it is based on. For several model parameters, data from different countries were not available. For example, the data from published reports from the US on duration of CKD4 [5] and ESRD survival data from the Dutch Renal Registry were used for all countries. To estimate transplantation rates, the national incidence data had to be inferred from the national prevalence data (as separate incidence data for dialysis and transplantation were not reported by national registries), and we were limited to Dutch data on the age pattern of transplantation rates. Moreover, research on the impact of CKD on the quality of life and productivity may not be representative across countries and cost differences within countries may be just as large as cost differences between countries. Third, the model is a crude model based on averages. It ignores more subtle relationships like the impact of the new treatment on the quality of life or the impact of prolongation of CKD4 on the transplantation rate and subsequent survival after RRT. For specific applications, models that are more sophisticated may need to be constructed. Fourth, the uncertainty of the model was not formally explored. Formal uncertainty analysis on the parameters of the model would be complex and would provide no information on the structural uncertainty of the model, or the uncertainty of the new intervention's preliminary estimates of the effectiveness and costs that are input to the model. For specific applications, the model is available as web content to perform several types of scenario analysis (www.lumc.nl/org/medische-besliskunde/medewerkers/​904030438095212).

Conclusion

This study provides evidence for the economic potential of therapies delaying ESRD. A 1-year delay of ESRD improves patient outcomes similarly over all countries assessed. Country-specific differences in costs are substantial. Generally, continuous use of medication is unlikely to be cost-effective for prices higher than €30,000 per year. The developed model provides developers and researchers information about the market success rates of treatment options at an early stage.

Acknowledgments

The research leading to these results has received funding from the European Community's Seventh Framework Program (FP7/2007-2013) under grant agreement number 305436 (STELLAR).

We would like to thank Frans van Ittersum and Aline Hemke for kindly providing data from Nefrovisie/Renine.

Disclosure Statement

The authors declare no conflicts of interests.

Statement of Ethics

No approval was required.


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Author Contacts

Wilbert B. van den Hout

Department of Medical Decision Making and Quality of Care

Leiden University Medical Center

PO Box 9600, NL-2300 RC Leiden (The Netherlands)

E-Mail w.b.van_den_hout@lumc.nl


Article / Publication Details

Received: December 08, 2015
Accepted: April 25, 2016
Published online: June 08, 2016
Issue release date: June 2016

Number of Print Pages: 9
Number of Figures: 5
Number of Tables: 3

ISSN: 1660-8151 (Print)
eISSN: 2235-3186 (Online)

For additional information: https://www.karger.com/NEF

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Abstract

Background. Low socio‐economic status is associated with the occurrence of several different chronic diseases, but evidence regarding renal disease is scant. To explore whether the risk of chronic renal failure varies by socio‐economic status, we performed a population‐based case‐control study in Sweden.

Methods. All native residents from May 1996 to May 1998, aged 18–74 years, formed the source population. Cases (n=926) were incident patients with chronic renal failure in a pre‐uraemic stage. Control subjects (n=998) were randomly selected within the source population. Exposures were assessed at personal interviews and relative risks were estimated by odds ratios (OR) in logistic regression models, with adjustment for age, sex, body mass index (BMI), smoking, alcohol consumption and regular analgesics use.

Results. In families with unskilled workers only, the risk of chronic renal failure was increased by 110% [OR=2.1; 95% confidence interval (CI), 1.1–4.0] and 60% (OR=1.6; 95% CI, 1.0–2.6) among women and men, respectively, relative to subjects living in families in which at least one member was a professional. Subjects with 9 years or less of schooling had a 30% (OR=1.3; 95% CI, 1.0–1.7) higher risk compared with those with a university education. The excess risk was of similar magnitude regardless of underlying renal disease.

Conclusions. Low socio‐economic status is associated with an increased risk of chronic renal failure. The moderate excess was not explained by age, sex, BMI, smoking, alcohol or analgesic intake. Thus, socio‐economic status appears to be an independent risk indicator for chronic renal failure in Sweden.

case‐control study, education, kidney failure, chronic, occupations, risk factors, socio‐economic factors

Introduction

Chronic renal failure (CRF) is a severe condition that reduces life expectancy and typically progresses to end‐stage renal disease (ESRD) and a need for renal replacement therapy. The prevalence of ESRD requiring treatment varies internationally from 600 to 1200 per million and is increasing steadily in most countries, with an estimated annual increase of 8%. In a large proportion of cases, CRF evolves from known renal or systemic diseases, but in some cases the pathogenesis remains unknown. Certain factors seem to promote CRF development irrespective of the underlying pathology: hypertension, proteinuria, hyperlipidaemia, high protein intake, smoking, heavy use of non‐narcotic analgesics and certain occupational exposures [1–4].

A gradient by socio‐economic status (SES) has been observed in a wide range of diseases [5]. SES represents an important risk indicator for cardiovascular disease, but evidence linking low SES to renal disease is scant. Earlier studies have shown an inverse association between income and treated ESRD [6–9]. The income‐earning capacity, however, is often reduced among ESRD patients and low income may be a consequence, rather than a cause, of ESRD.

To shed light on the possible associations between SES and the risk of CRF, we analysed this exposure in relation to incident pre‐uraemic disease in a nation‐wide, population‐based case‐control study in Sweden. An occupational‐based socio‐economic classification scale and educational level were used as measures of SES.

Subjects and methods

Setting

In Sweden, the county councils provide health care at hospitals and primary health care centres to all residents. Out‐of‐pocket charges are kept low enough to ensure equal health care access. The study base was well defined through the continuously updated National Population Register and comprised 5.3 million native Swedes, aged 18–74 years, resident in the country during the ascertainment period 20 May 1996 to 31 May 1998.

Subjects

Monthly lists of all serum creatinine measurements were provided by medical laboratories covering essentially all inpatient and outpatient care in Sweden. Eligible as cases were patients in the source population whose serum creatinine level for the first time, and permanently, exceeded 300 µmol/l (3.4 mg/dl) for men or 250 µmol/l (2.8 mg/dl) for women, the increase being due to renal causes. Case patient eligibility was determined in collaboration with local physicians. Patients with pre‐renal causes (e.g. severe heart failure) or post‐renal causes (e.g. outlet obstruction) of the serum creatinine elevation were not eligible. Diagnoses of underlying conditions were based on routine clinical work‐up.

Control subjects, frequency‐matched to the case patients by age and sex, were randomly selected from the National Population Register on three occasions during the ascertainment period.

All regional ethics committees and the Swedish Data Inspection Board approved the study protocol. Each study subject provided informed consent before inclusion.

Data collection

The study subjects received a mailed self‐administered questionnaire about number of years of education, highest educational degree, marital status, anthropometrical measures, weekly alcohol intake and lifetime tobacco use. Information on every occupation held for more than a year was obtained during subsequent face‐to‐face interviews by professional interviewers from Statistics Sweden. In addition, occupations of spouses and parents were recorded to assess ‘household SES’. A detailed history of lifetime use of non‐narcotic analgesics was also obtained during the interview, as described elsewhere [4]. The interviewers could not be kept blinded to the case control status of the interviewees, but they were unaware of the study hypotheses, and they were trained to treat both categories in a strictly equal manner. The mailed questionnaire was checked during the interview and supplemented when needed.

Statistical analysis

Occupation and educational level were used independently to estimate SES. The SES associated with reported occupations was derived from the official Swedish socio‐economic classification scheme (SEI), and the scores were aggregated into the following classes: (i) unskilled and semi‐skilled manual workers; (ii) skilled manual workers; (iii) assistant non‐manual employees; (iv) intermediate non‐manual employees; (v) employed or self‐employed professionals, higher civil servants and executives. A sixth group of self‐employed (other than professionals) and farmers was analysed separately. Each subject was grouped according to the highest SEI score obtained from the occupational history. The spouse with the highest score determined the household SES. Students were classified according to the SES of their parents. Educational level was grouped into three categories based on the number of years of education (0–9, 10–12 and 13 years or more).

Multivariate unconditional logistic regression models estimated relative risks [odds ratios (OR)] as measures of the association between SES and CRF, along with their 95% confidence intervals (CI). Co‐variates were considered if they were known or suspected a priori to be confounding factors, or if they were associated with both CRF and SES in the data. We initially considered height, body‐mass index (BMI; the weight in kilograms divided by the square of the height in metres), number of siblings, cigarette smoking, weekly alcohol consumption and cumulative lifetime dose of analgesics during regular use. Smoking and alcohol consumption were grouped in quartiles according to the distribution among control subjects.

The final analysis model, assessed using the likelihood ratio test, contained terms for age, sex, BMI, cigarette smoking, alcohol consumption and ever vs never regular use of aspirin or paracetamol use. A simple indicator variable (ever vs never) of regular use was found to sufficiently control for possible confounding by aspirin or acetaminophen in relation to SES. Regular use of an analgesic was defined as use at least twice a week for 2 months or longer. In our modelling, we excluded 37 case patients (4.0%) and 48 control subjects (4.8%) with missing information for one or more co‐variates. Analysis of variance was used to investigate the relation between SES and glomerular filtration rate.

Results

During the study period, we identified 1189 eligible patients and 35 whose eligibility could not be established. Nine hundred and twenty‐six patients (78%) participated, while 111 refused, 83 had severe diseases that precluded participation and 69 (6%) died shortly after diagnosis. Of the 1330 eligible control subjects sampled, 998 (75%) participated, 221 refused, 56 could not be reached and 55 had severe diseases that precluded participation.

Characteristics of the participating case patients and control subjects are shown in Table 1. There were about twice as many men as women. As expected from the frequency‐matched design, the mean age was identical among case patients and control subjects. Further, the mean age was similar among men (58 years) and women (57 years). Patients and control subjects did not differ materially with regard to number of reported occupations (median=3 in both groups). Thirty‐one percent of the case patients were classified as having diabetic nephropathy, 24% had a glomerulonephritis diagnosis, 15% renal vascular disease, 11% a hereditary renal disease, 9% a systemic disease or vasculitis and 11% any other renal disease diagnosis. A majority of the patients were in the pre‐uraemic stage, in no need of renal replacement therapy. The median value of the estimated glomerular filtration rate (GFR) was 21 ml/min (interquartile range, 17–26 ml/min). Among patients in the unskilled manual workers group, 27% had a GFR in the lowest quartile, 24% in the second quartile, 27% in the third and 21% had a GFR in the highest quartile. The corresponding distribution of the GFR values among patients in the professionals group were 20% in the lowest quartile, 25% in the second, 23% in the third and 33% in the highest quartile. No statistically significant differences in GFR between patients in the different SES groups were found (P=0.07).

Table 2 shows the distributions by SES of alcohol consumption, cigarette smoking and a diagnosis of hypertension or diabetes mellitus among the control subjects. The largest proportion of reported alcohol abstinence was found among the unskilled manual worker (30.1%) and self‐employed subjects (31.8%), while weekly alcohol consumption within the highest quartile (>66 g alcohol (∼75 cl wine) per week) was most common among the skilled manual subjects (23.3%) and the professionals (25.4%). The proportion of never smokers was largest among the professionals (50.9%), and in the heterogeneous group of self‐employed subjects and farmers (56.8%). Unskilled manual workers and skilled manual workers had the largest proportion of subjects (∼18%) classified in the heaviest smoking category (>27.5 pack‐years). The variation was small in the proportions of reported hypertension or diabetes diagnoses between the SES groups. Regular use of analgesics such as paracetamol or aspirin was most common among control subjects in the unskilled manual worker group (30.1%) and least common among the professionals (20.0%) (data not shown). More than 50% of the subjects categorized as skilled manual workers, assistant non‐manual employees or self‐employed had a BMI >25 (data not shown).

The risk of CRF was inversely related to SES inferred from occupations of the individual study participants (Table 3). The relationship with household SES was even clearer. The gradients among women were at least as marked as those among men. Compared with women in families with the highest SES, female members of families with unskilled workers only had a 110% (OR 2.1; 95% CI 1.1–4.0) excess risk for CRF following adjustments for potential confounding factors. The corresponding excess among men was 60% (OR 1.6; 95% CI 1.0–2.6). Subjects with 9 years or less of schooling had a 30% (OR 1.3; 95% CI 1.0–1.7) higher risk compared with those who went to university, but this excess was mainly confined to men. Compared with crude estimates of the associations between SES and CRF, the combined adjustments generally tended to move the point estimates moderately towards unity, in most instances less than 20% (data not shown).

Table 4 shows the relationship by underlying pathology. Household SES was associated with a >2‐fold risk gradient for all major types of CRF, although the dose–response curve varied somewhat in appearance between diagnostic categories. The relationship appeared less convincing for the miscellaneous group of underlying pathology. The trend with individual level of education was weaker for CRF classified as glomerulonephritis than for the other major types and it was absent for the miscellaneous group.

Table 1. 

Characteristics of the study subjects

Variable
Cases n=926
Controls n=998
Household SESann
 professionals 138 14.9 210 21.0 
 intermediate non‐manual 171 18.5 215 21.5 
 assistant non‐manual 183 19.8 175 17.5 
 skilled manual 232 25.1 206 20.6 
 unskilled manual 136 14.7 103 10.3 
 self‐employed 58 6.3 88 8.8 
 missing data 0.9 0.1 
Years of education nn
 ≥13 168 18.1 220 22.0 
 10–12 209 22.6 246 24.7 
 ≤9 537 58.0 525 52.6 
Sexbnn
 male 597 64.5 653 65.4 
 female 329 35.5 345 34.6 
Age at interview (years)bnn
 18–24 11 1.2 20 2.0 
 25–34 63 6.8 58 5.8 
 35–44 95 10.3 97 9.7 
 45–54 193 20.8 186 18.6 
 55–64 186 20.1 204 20.4 
 65–74 378 40.8 433 43.4 
Weekly alcohol intake (g) nn
 none 234 25.3 202 20.2 
 ≤17.6 217 23.4 258 25.8 
 17.7–33 116 12.5 135 13.5 
 33.1–66 143 15.4 190 19.0 
 >66 206 22.2 209 20.9 
 missing 10 1.1 0.4 
Variable
Cases n=926
Controls n=998
Household SESann
 professionals 138 14.9 210 21.0 
 intermediate non‐manual 171 18.5 215 21.5 
 assistant non‐manual 183 19.8 175 17.5 
 skilled manual 232 25.1 206 20.6 
 unskilled manual 136 14.7 103 10.3 
 self‐employed 58 6.3 88 8.8 
 missing data 0.9 0.1 
Years of education nn
 ≥13 168 18.1 220 22.0 
 10–12 209 22.6 246 24.7 
 ≤9 537 58.0 525 52.6 
Sexbnn
 male 597 64.5 653 65.4 
 female 329 35.5 345 34.6 
Age at interview (years)bnn
 18–24 11 1.2 20 2.0 
 25–34 63 6.8 58 5.8 
 35–44 95 10.3 97 9.7 
 45–54 193 20.8 186 18.6 
 55–64 186 20.1 204 20.4 
 65–74 378 40.8 433 43.4 
Weekly alcohol intake (g) nn
 none 234 25.3 202 20.2 
 ≤17.6 217 23.4 258 25.8 
 17.7–33 116 12.5 135 13.5 
 33.1–66 143 15.4 190 19.0 
 >66 206 22.2 209 20.9 
 missing 10 1.1 0.4 

View Large

Table 2. 

Distribution of weekly alcohol intake, lifetime smoking and diagnosis of hypertension or diabetes mellitus among 997 control subjects by socio‐economic groupa

Variable
Unskilled manual (n=103)
Skilled manual (n=206)
Assistant non‐manual (n=175)
Intermediate non‐manual (n=215)
Professionals (n=210)
Self‐employed (n=88)
Weekly alcohol intakebn (%) n (%) n (%) n (%) n (%) n (%) 
 none 31 (30.1) 49 (23.8) 33 (18.9) 42 (19.5) 19 (9.0) 28 (31.8) 
 ≤17.6 g 32 (31.1) 46 (22.3) 42 (24.0) 62 (28.8) 55 (26.2) 21 (23.9) 
 17.7–33 g 8 (7.8) 39 (18.9) 24 (13.7) 29 (13.5) 31 (14.8) 4 (4.5) 
 33.1–66 g 16 (15.5) 23 (11.2) 40 (22.9) 37 (17.2) 55 (26.2) 19 (21.6) 
 >66 g 15 (14.6) 48 (23.3) 36 (20.6) 44 (20.5) 50 (23.8) 16 (18.2) 
 missing 1 (1.0) 1 (0.5) 1 (0.5) 
Smokingcn (%) n (%) n (%) n (%) n (%) n (%) 
 none 42 (40.8) 75 (36.4) 69 (39.4) 97 (45.1) 107 (50.9) 50 (56.8) 
 ≤6.6 11 (10.7) 30 (14.6) 27 (15.4) 34 (15.8) 25 (11.9) 10 (11.4) 
 6.7–16 14 (13.6) 26 (12.6) 30 (17.1) 28 (13.0) 27 (12.9) 15 (17.0) 
 16.1–27.5 13 (12.6) 37 (18.0) 21 (12.0) 28 (13.0) 35 (16.7) 7 (7.9) 
 >27.5 19 (18.4) 36 (17.5) 27 (15.4) 27 (12.6) 15 (7.1) 6 (6.8) 
 missing 4 (3.9) 2 (1.0) 1 (0.6) 1 (0.5) 1 (0.5) 
Hypertension n (%) n (%) n (%) n (%) n (%) n (%) 
 no 83 (80.6) 167 (81.1) 141 (80.6) 184 (85.6) 180 (85.7) 73 (83.0) 
 yes 19 (18.4) 36 (17.5) 34 (19.4) 31 (14.4) 30 (14.3) 14 (15.9) 
 missing 1 (1.0) 3 (1.5) 1 (1.1) 
Diabetes mellitus n (%) n (%) n (%) n (%) n (%) n (%) 
 no 97 (94.2) 188 (91.3) 161 (92.0) 200 (93.0) 199 (94.8) 84 (95.4) 
 yes 6 (5.8) 18 (8.7) 14 (8.0) 15 (7.0) 11 (5.2) 4 (4.6) 
Variable
Unskilled manual (n=103)
Skilled manual (n=206)
Assistant non‐manual (n=175)
Intermediate non‐manual (n=215)
Professionals (n=210)
Self‐employed (n=88)
Weekly alcohol intakebn (%) n (%) n (%) n (%) n (%) n (%) 
 none 31 (30.1) 49 (23.8) 33 (18.9) 42 (19.5) 19 (9.0) 28 (31.8) 
 ≤17.6 g 32 (31.1) 46 (22.3) 42 (24.0) 62 (28.8) 55 (26.2) 21 (23.9) 
 17.7–33 g 8 (7.8) 39 (18.9) 24 (13.7) 29 (13.5) 31 (14.8) 4 (4.5) 
 33.1–66 g 16 (15.5) 23 (11.2) 40 (22.9) 37 (17.2) 55 (26.2) 19 (21.6) 
 >66 g 15 (14.6) 48 (23.3) 36 (20.6) 44 (20.5) 50 (23.8) 16 (18.2) 
 missing 1 (1.0) 1 (0.5) 1 (0.5) 
Smokingcn (%) n (%) n (%) n (%) n (%) n (%) 
 none 42 (40.8) 75 (36.4) 69 (39.4) 97 (45.1) 107 (50.9) 50 (56.8) 
 ≤6.6 11 (10.7) 30 (14.6) 27 (15.4) 34 (15.8) 25 (11.9) 10 (11.4) 
 6.7–16 14 (13.6) 26 (12.6) 30 (17.1) 28 (13.0) 27 (12.9) 15 (17.0) 
 16.1–27.5 13 (12.6) 37 (18.0) 21 (12.0) 28 (13.0) 35 (16.7) 7 (7.9) 
 >27.5 19 (18.4) 36 (17.5) 27 (15.4) 27 (12.6) 15 (7.1) 6 (6.8) 
 missing 4 (3.9) 2 (1.0) 1 (0.6) 1 (0.5) 1 (0.5) 
Hypertension n (%) n (%) n (%) n (%) n (%) n (%) 
 no 83 (80.6) 167 (81.1) 141 (80.6) 184 (85.6) 180 (85.7) 73 (83.0) 
 yes 19 (18.4) 36 (17.5) 34 (19.4) 31 (14.4) 30 (14.3) 14 (15.9) 
 missing 1 (1.0) 3 (1.5) 1 (1.1) 
Diabetes mellitus n (%) n (%) n (%) n (%) n (%) n (%) 
 no 97 (94.2) 188 (91.3) 161 (92.0) 200 (93.0) 199 (94.8) 84 (95.4) 
 yes 6 (5.8) 18 (8.7) 14 (8.0) 15 (7.0) 11 (5.2) 4 (4.6) 

View Large

Table 3. 

The risk of CRF in relation to SES

Variable Men
Women
Both sexes

Cases (n=597)
Controls (n=653)
Cases (n=329)
Controls (n=345)
Household SESannORb95% CIcnnORb95% CIcORb95% CIc
 professionals 100 147 1.0 Ref 38 63 1.0 Ref 1.0 Ref 
 intermediate non‐manual 120 135 1.2 (0.8–1.7) 51 80 0.9 (0.5–1.6) 1.1 (0.8–1.4) 
 assistant non‐manual 97 103 1.3 (0.9–2.0) 86 72 1.5 (0.9–2.7) 1.4 (1.0–1.9) 
 skilled manual 170 153 1.5 (1.0–2.1) 62 53 1.5 (0.8–2.7) 1.5 (1.1–2.0) 
 unskilled manual 65 59 1.6 (1.0–2.6) 71 44 2.1 (1.1–4.0) 1.9 (1.3–2.7) 
 self‐employed 39 55 1.1 (0.6–1.8) 19 33 0.7 (0.3–1.5) 0.9 (0.6–1.4) 
Individual SESdnnORb95% CIcnnORb95% CIcORb95% CIc
 professionals 86 128 1.0 Ref 22 34 1.0 Ref 1.0 Ref 
 intermediate non‐manual 95 117 1.1 (0.8–1.7) 40 73 0.9 (0.4–1.8) 1.0 (0.7–1.4) 
 assistant non‐manual 72 72 1.5 (0.9–2.3) 90 81 1.6 (0.8–3.1) 1.5 (1.1–2.2) 
 skilled manual 227 194 1.6 (1.1–2.3) 43 51 1.1 (0.5–2.3) 1.5 (1.1–2.0) 
 unskilled manual 73 75 1.4 (0.9–2.3) 111 81 1.9 (0.9–3.8) 1.7 (1.2–2.5) 
 self‐employed 30 48 1.0 (0.6–1.8) 11 0.7 (0.2–2.3) 1.0 (0.6–1.6) 
Years of education nnORb95% CIcnnORb95% CIcORb95% CIc
 ≥13 years 109 142 1.0 Ref 59 78 1.0 Ref 1.0 Ref 
 10–12 years 129 150 1.2 (0.8–1.7) 80 96 0.9 (0.6–1.5) 1.1 (0.8–1.4) 
 ≤9 years 350 355 1.4 (1.0–1.9) 187 170 1.1 (0.7–1.9) 1.3 (1.0–1.7) 
Variable Men
Women
Both sexes

Cases (n=597)
Controls (n=653)
Cases (n=329)
Controls (n=345)
Household SESannORb95% CIcnnORb95% CIcORb95% CIc
 professionals 100 147 1.0 Ref 38 63 1.0 Ref 1.0 Ref 
 intermediate non‐manual 120 135 1.2 (0.8–1.7) 51 80 0.9 (0.5–1.6) 1.1 (0.8–1.4) 
 assistant non‐manual 97 103 1.3 (0.9–2.0) 86 72 1.5 (0.9–2.7) 1.4 (1.0–1.9) 
 skilled manual 170 153 1.5 (1.0–2.1) 62 53 1.5 (0.8–2.7) 1.5 (1.1–2.0) 
 unskilled manual 65 59 1.6 (1.0–2.6) 71 44 2.1 (1.1–4.0) 1.9 (1.3–2.7) 
 self‐employed 39 55 1.1 (0.6–1.8) 19 33 0.7 (0.3–1.5) 0.9 (0.6–1.4) 
Individual SESdnnORb95% CIcnnORb95% CIcORb95% CIc
 professionals 86 128 1.0 Ref 22 34 1.0 Ref 1.0 Ref 
 intermediate non‐manual 95 117 1.1 (0.8–1.7) 40 73 0.9 (0.4–1.8) 1.0 (0.7–1.4) 
 assistant non‐manual 72 72 1.5 (0.9–2.3) 90 81 1.6 (0.8–3.1) 1.5 (1.1–2.2) 
 skilled manual 227 194 1.6 (1.1–2.3) 43 51 1.1 (0.5–2.3) 1.5 (1.1–2.0) 
 unskilled manual 73 75 1.4 (0.9–2.3) 111 81 1.9 (0.9–3.8) 1.7 (1.2–2.5) 
 self‐employed 30 48 1.0 (0.6–1.8) 11 0.7 (0.2–2.3) 1.0 (0.6–1.6) 
Years of education nnORb95% CIcnnORb95% CIcORb95% CIc
 ≥13 years 109 142 1.0 Ref 59 78 1.0 Ref 1.0 Ref 
 10–12 years 129 150 1.2 (0.8–1.7) 80 96 0.9 (0.6–1.5) 1.1 (0.8–1.4) 
 ≤9 years 350 355 1.4 (1.0–1.9) 187 170 1.1 (0.7–1.9) 1.3 (1.0–1.7) 

View Large

Table 4. 

The risk of type‐specific CRF in relation to SES

Variable
Diabetic nephropathy (n=286)


Glomerulonephritis (n=222)


Renal vascular disease (n=139)


Othera (n=279)
Household SESdnORb95% CIcnORb95% CIcnORb95% CIcnORb95% CIc
 professionals 36 1.0 ref 39 1.0 ref 13 1.0 ref 50 1.0 Ref 
 intermediate non‐manual 42 0.8 (0.5–1.4) 39 1.0 (0.6–1.6) 31 2.2 (1.1–4.5) 59 1.1 (0.7–1.7) 
 assistant non‐manual 56 1.5 (0.9–2.5) 40 1.4 (0.8–2.3) 30 2.5 (1.2–5.2) 57 1.2 (0.8–2.0) 
 skilled manual 84 1.8 (1.1–2.8) 53 1.3 (0.8–2.1) 35 2.4 (1.1–4.8) 60 1.2 (0.8–1.9) 
 unskilled manual 49 2.4 (1.4–4.2) 35 2.2 (1.3–3.8) 18 2.5 (1.1–5.7) 34 1.4 (0.8–2.4) 
 self‐employed 15 0.9 (0.5–1.9) 14 1.0 (0.5–2.1) 12 2.0 (0.8–4.9) 17 0.8 (0.4–1.6) 
Individual SESenORb95% CIcnORb95% CIcnORb95% CIcnORb95% CIc
 professionals 31 1.0 Ref 31 1.0 Ref 10 1.0 Ref 36 1.0 Ref 
 intermediate non‐manual 36 0.8 (0.5–1.4) 34 1.1 (0.6–1.8) 20 1.9 (0.8–4.4) 45 1.0 (0.6–1.7) 
 assistant non‐manual 50 1.6 (0.9–2.7) 31 1.5 (0.8–2.6) 26 2.6 (1.1–6.0) 55 1.5 (0.9–2.4) 
 skilled manual 83 1.4 (0.8–2.2) 65 1.4 (0.9–2.4) 52 3.1 (1.4–6.6) 70 1.3 (0.8–2.1) 
 unskilled manual 67 2.1 (1.2–3.5) 45 2.1 (1.2–3.7) 22 2.2 (0.9–5.1) 50 1.4 (0.8–2.3) 
 self‐employed 10 0.8 (0.4–1.9) 0.9 (0.4–2.1) 2.3 (0.8–6.6) 10 0.9 (0.4–2.1) 
Years of education nORb95% CIcnORb95% CIcnORb95% CIcnORb95% CIc
 ≥13 years 38 1.0 ref 43 1.0 ref 12 1.0 ref 75 1.0 Ref 
 10–12 years 61 1.5 (0.9–2.4) 65 1.4 (0.9–2.2) 24 1.6 (0.7–3.4) 59 0.7 (0.5–1.1) 
 ≤9 years 181 2.0 (1.2–3.2) 112 1.4 (0.9–2.3) 101 2.4 (1.2–4.7) 143 0.8 (0.5–1.2) 
Variable
Diabetic nephropathy (n=286)


Glomerulonephritis (n=222)


Renal vascular disease (n=139)


Othera (n=279)
Household SESdnORb95% CIcnORb95% CIcnORb95% CIcnORb95% CIc
 professionals 36 1.0 ref 39 1.0 ref 13 1.0 ref 50 1.0 Ref 
 intermediate non‐manual 42 0.8 (0.5–1.4) 39 1.0 (0.6–1.6) 31 2.2 (1.1–4.5) 59 1.1 (0.7–1.7) 
 assistant non‐manual 56 1.5 (0.9–2.5) 40 1.4 (0.8–2.3) 30 2.5 (1.2–5.2) 57 1.2 (0.8–2.0) 
 skilled manual 84 1.8 (1.1–2.8) 53 1.3 (0.8–2.1) 35 2.4 (1.1–4.8) 60 1.2 (0.8–1.9) 
 unskilled manual 49 2.4 (1.4–4.2) 35 2.2 (1.3–3.8) 18 2.5 (1.1–5.7) 34 1.4 (0.8–2.4) 
 self‐employed 15 0.9 (0.5–1.9) 14 1.0 (0.5–2.1) 12 2.0 (0.8–4.9) 17 0.8 (0.4–1.6) 
Individual SESenORb95% CIcnORb95% CIcnORb95% CIcnORb95% CIc
 professionals 31 1.0 Ref 31 1.0 Ref 10 1.0 Ref 36 1.0 Ref 
 intermediate non‐manual 36 0.8 (0.5–1.4) 34 1.1 (0.6–1.8) 20 1.9 (0.8–4.4) 45 1.0 (0.6–1.7) 
 assistant non‐manual 50 1.6 (0.9–2.7) 31 1.5 (0.8–2.6) 26 2.6 (1.1–6.0) 55 1.5 (0.9–2.4) 
 skilled manual 83 1.4 (0.8–2.2) 65 1.4 (0.9–2.4) 52 3.1 (1.4–6.6) 70 1.3 (0.8–2.1) 
 unskilled manual 67 2.1 (1.2–3.5) 45 2.1 (1.2–3.7) 22 2.2 (0.9–5.1) 50 1.4 (0.8–2.3) 
 self‐employed 10 0.8 (0.4–1.9) 0.9 (0.4–2.1) 2.3 (0.8–6.6) 10 0.9 (0.4–2.1) 
Years of education nORb95% CIcnORb95% CIcnORb95% CIcnORb95% CIc
 ≥13 years 38 1.0 ref 43 1.0 ref 12 1.0 ref 75 1.0 Ref 
 10–12 years 61 1.5 (0.9–2.4) 65 1.4 (0.9–2.2) 24 1.6 (0.7–3.4) 59 0.7 (0.5–1.1) 
 ≤9 years 181 2.0 (1.2–3.2) 112 1.4 (0.9–2.3) 101 2.4 (1.2–4.7) 143 0.8 (0.5–1.2) 

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Discussion

In this large case‐control study, household SES emerged as a significant risk indicator for CRF, independent of factors such as age, sex, BMI, cigarette smoking, alcohol intake and use of aspirin or paracetamol. The risk gradient from highest to lowest socio‐economic stratum was similar for diseases as different as diabetic nephropathy, glomerulonephritis and renal vascular disease, but the detailed trend pattern varied with underlying pathology.

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