Background: The number of patients receiving renal replacement therapy (RRT) increases annually and worldwide. Differences in the RRT incidence, prevalence, and modality vary between regions and countries for reasons yet to be clarified. Aims: Gain a better understanding of the association between hemodialysis (HD)-related variables and general population global health indicators. Methods: The present study included prevalent HD patients from 27 countries/regions from the monitoring dialysis outcomes (MONDO) database from 2006-2011. Global population health indicators were obtained from the 2014 World Health Organization report and the Human Development Index from the Human Development Report Office 2014. The Spearman rank test was used to assess the correlations between population social economic indicators and HD variables. Results: A total of 84,796 prevalent HD patients were included. Their mean age was 63 (country mean 52-71), and 60% were males (country mean 52-85%). Significant correlations were found between HD demographic clusters and population education, wealth, mortality, and health indicators. The cluster of nutrition and inflammation variables were also highly correlated with population mortality, wealth, and health indicators. Finally, cardiovascular, fluid management, and dialysis adequacy clusters were associated with education, wealth, and health care resource indicators. Conclusion: We identified socioeconomic indicators that were correlated with dialysis variables. This hypothesis-generating study may be helpful in the analysis of how global health indicators may interfere with access to HD, treatment provision, dialytic treatment characteristics, and outcomes.

Renal replacement therapy (RRT), namely hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation has been used since the 1940s to treat end-stage renal disease (ESRD) patients worldwide, and access to RRT is available in an increasing number of countries [1,2,3,4]. The number of patients on RRT has increased gradually, particularly in developing countries [5,6,7,8]. One of the most important reasons for this is because a broader population now has access to dialysis ensuring almost universal coverage including areas where previously patients had no access to treatment [7,8,9].

Differences in the RRT incidence, prevalence, and modality use vary broadly depending on region or country characteristics [10,11,12]. This can be partly explained by differences in social economic factors such as per capita gross domestic product (GDP), the proportion of GDP spend on health care, life expectancy at birth, and human development index (HDI) [9,13,14,15]. The prevalence of RRT has increased particularly in countries with public health care systems and RRT insurance coverage, while the incidence increases independently of the degree of population coverage [9,15,16].

Patient characteristics and dialysis practices also vary widely between countries [17]. Despite the growing number of comprehensive reports on ESRD populations and dialysis treatments at national and international levels such as United States Renal Data System (USRDS) reports, the European Renal Association European Dialysis and Transplant Association (ERA-EDTA) reports, the Latin America Dialysis and Renal Transplant Registry (RLADTR), and Dialysis Outcomes and Practice Patterns Study (DOPPS) reports, there is still lack of information about the patterns of patient characteristics and dialysis variables in different areas of the world [18].

One of the most recent initiatives designed to improve our understanding of the epidemiology in patients with ESRD globally is the MONitoring Dialysis Outcome (MONDO) Initiative. MONDO, a consortium of multiple dialysis providers around the world, covering more than 40 countries, provides information about patients' demographic parameters, treatment characteristics, laboratory results, prescription data, as well as outcomes such as mortality and hospitalization [19,20].

We hypothesize that global population health indicators may be associated with RRT access, treatment provision, dialytic treatment characteristics, and outcomes. The aim of this study was to analyze the association between global population health indicators and ESRD patient profiles and characteristics in the MONDO cohort.

The study included prevalent ESRD patients undergoing maintenance HD from 27 countries/regions in the MONDO database from 2006 to 2011. Demographic and laboratory data were analyzed from dialysis unit records. Patients were considered prevalent when they survived at least one year on RRT. The average of years 1-3 on dialysis was computed for continuous variables. Data concerning socioeconomic status indicators from each participating country were obtained from the 2014 World Health Organization's (WHO) global health report; data concerning Human Development Index (HDI) were obtained from the Human Development Report Office (HDRO) [21,22] (table 1).

Table 1

Socioeconomic indicators

Socioeconomic indicators
Socioeconomic indicators

HDI is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and enjoying a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The country classification according to HDI defines very high HDI ≥0.8; high HDI between 0.79 and 0.7; median HDI between 0.7 and 0.55, and low <0.55. All countries with HD patient information documented in the MONDO database and global health indicators documented in the WHO and HDRO reports were included in the analysis.

In total, we analyzed 40 dialysis-related and 52 socioeconomic variables. These 92 variables were grouped into 6 dialysis-related and 6 population socioeconomic clusters. The six dialysis-related clusters were (a) demographics, (b) comorbidities, (c) nutrition and inflammation, (d) anemia, (e) mineral metabolism and electrolytes, and (f) cardiovascular/fluid management/dialysis adequacy. The six global population health clusters were (a) demographics, (b) education, (c) wealth, (d) population risk factors, (e) health care resources, and (f) mortality. The variables included in the respective clusters are shown in (tables 2, 3).

Table 2

Socioeconomic clusters

Socioeconomic clusters
Socioeconomic clusters
Table 3

Dialysis clusters

Dialysis clusters
Dialysis clusters

Descriptive analyses for continuous variables were reported as mean, minimum and maximum, and standard deviations. For categorical variables, the percentage of each category was reported. The Spearman rank test was used to access the correlations between social economic indicators and dialysis variables. A two-sided p < 0.05 was considered significant. In total, we computed 2,080 correlations. We then calculated the fraction of significant correlations (p < 0.05) per cluster. The fraction of significant correlations differed between clusters, ranging from 4.2 to 81.3%, with a median of 20%. Here we limit the presentation to HD clusters, which had at least 2 socioeconomic clusters with more than 20% of significant correlations. A correlation matrix plot was constructed to present the results. All the analyses were performed in R program - version 3.1.1.

Data from 27 countries were used in these analyses. A total of 84,796 prevalent ESRD patients on HD were included. Their mean age was 63 years (country means 52-71), with the highest mean age in Hong Kong and the lowest in Russia. The mean percentage of males in this population was 61% (country means 52-85%) with the highest percentage in Sweden (85%) and the lowest in Taiwan (52%). The proportion of diabetic patients was highest in the United States (54%) and lowest in Sweden (5%). The average percentage of others comorbidities and dialysis parameters per country is shown in tables 4, (5), 6.

Table 4

Percentage of HD patient comorbidities per country/regions

Percentage of HD patient comorbidities per country/regions
Percentage of HD patient comorbidities per country/regions
Table 5

HD patient comorbidities (in %), countries combined

HD patient comorbidities (in %), countries combined
HD patient comorbidities (in %), countries combined
Table 6

HD variables; countries combined

HD variables; countries combined
HD variables; countries combined

Global population health indicators were extracted from the WHO 2014 report and from the HDRO report (table 1). Most countries in this analysis have very high (17 countries) or high (9 countries) HDI, while it is low in the Philippines. Significant correlations between global health indicators and dialysis variables are presented in a correlation matrix plot (fig. 1).

Fig. 1

The correlation matrix of HD variables and socioeconomic indicators. Blue color indicates positive correlation; red color indicates negative correlation, the color intensity and the shape of the symbols are both indicators of the strength of the correlation (square: correlation coefficient <0.54; circle: between 0.54-0.07 and star: ≥0.7). Green lines delineate the clusters. Only correlations with p < 0.05 are shown. Abbreviations are shown in tables 2 and 3.

Fig. 1

The correlation matrix of HD variables and socioeconomic indicators. Blue color indicates positive correlation; red color indicates negative correlation, the color intensity and the shape of the symbols are both indicators of the strength of the correlation (square: correlation coefficient <0.54; circle: between 0.54-0.07 and star: ≥0.7). Green lines delineate the clusters. Only correlations with p < 0.05 are shown. Abbreviations are shown in tables 2 and 3.

Close modal

We found significant correlations between dialysis demographics cluster and mortality, wealth, and education clusters of population with a percentage of 81, 63, and 50% of correlations performed, respectively. The dialysis-related nutrition and inflammation cluster showed a higher fraction of significant correlations with mortality (40%), wealth (31%), education (25%), and population-risk factors (25%). The dialysis-related anemia cluster was correlated with wealth (33%), health care resources (22%), mortality (21%), and population-risk factors (21%). The cardiovascular/fluid management/dialysis adequacy cluster correlated with education (41%), wealth (31%), and health care resources (25%). Other clusters did not show more than 20% of significant correlations.

Differences in the RRT incidence, prevalence, and modality vary depending on socioeconomic status of the region. In 2009, Yoshino et al. reported on a global level the correlations between mortalities in the general and HD populations in the respective countries [13]. In the present study, we extend that analysis by exploring associations between global population health indicators and dialysis variables in the international MONDO HD patient cohort.

Patient demographics, anemia, nutrition, and cardiovascular assessment were HD clusters with large numbers of significant correlations associated with clusters of global health indicators.

Mortality, wealth, education, and health care resources indices were significantly correlated with the demographic dialysis cluster. The demographic variables associated with global health indicators included particularly age and percentage of males. These findings corroborate reports from the general population, since age, gender, and race are directly related to mortality, wealth, education, and health care resources [21,22]. The positive correlation coefficients between HD demographics and mortality (represented by life expectancy at birth and life expectancy at 60 years of age) could reflect an increase in access to RRT around the world, as well as an improvement in access to pre-dialysis care which delays HD initiation and extends survival [1,2,3,4,5,6,7,8,9]. The gross national income (GNI) from wealth cluster, the percentage of primary school enrolment in the general population, from education cluster, and the per capita total expenditure on health (US$), and per capita government expenditure on health (PPP int. $) from health care resources cluster were all positively correlated with the demographic cluster in dialysis population. RRT is an expensive therapy and can be responsible for 1-2% of health care expenditure in high income countries [23,24]. Data from EVEREST study already showed the incidence of patients starting RRT is influenced by some certain macroeconomic factors including national wealth and economic structure [14,15].

Particularly albumin and LDL cholesterol (LDL-C) as nutritional and inflammatory parameters (which are usually related to each other [24]) showed significant correlations with mortality, wealth, education, and population-risk factors. Albumin is a surrogate marker of nutrition and inflammation strongly associated with HD mortality [25]. In a large international study of the MONDO cohort, Usvyat et al. showed a significant decrease of albumin levels in HD patients a few months before death [26]. LDL-C is more closely related to vascular calcification and the risk increase for arteriosclerosis and cardiovascular diseases, the leading cause of death in HD [27,28]. Notable are the negative correlations between albumin and LDL-C with life expectancy at birth, life expectancy at 60 years, HDI and gross national income (GNI), and also between LDL-C and primary school enrolment.

The anemia cluster was highly correlated with wealth, health care resources, population risk factors, and mortality clusters. The hemoglobin (Hb) level was the principle variable and was positively correlated with HDI and GNI and negatively correlated with poverty. Hb was also positively correlated with the number of physicians, nurses, pharmacists, dentists, and many variables related with health care expenditure, as well as with the population-risk factors percentage of population drinking water and sanitation. On the other hand, Hb levels were generally negatively correlated with mortality cluster variables neonatal death, death below age 5, and adult mortality. Anemia is associated with mortality rate in the general population at different ages and can be related to diseases such as ESRD, malignancy, and heart disease [29,30]. An effective treatment for anemia in ESRD patients depends on the availability of expensive medications, such as erythropoiesis stimulating agents (ESAs) and intravenous iron. Provision of these may be scarce in some countries when the consequence Hb target levels cannot be achieved and may affect patient outcomes [31,32].

Finally, the variables of the cluster ‘cardiovascular, fluid management and dialysis adequacy' were strongly correlated with the education, wealth and heath care resources clusters. Some studies have shown that the degree of literacy is associated with dialysis outcomes [33]. The cardiovascular, fluid management and dialysis adequacy cluster and the education cluster as represented by literacy and primary school enrolment were negatively correlated. Some examples from the cardiovascular, fluid management, and dialysis adequacy cluster are the significant correlations of the lower IDWG and better blood pressure control with the higher degree of literacy and primary school enrolment. A positive correlation between HD prevalence and wealth was previously described, showing that the lower the GPD (under US$ 10,000), the higher the prevalence of ESRD [6,7]. Wealth can also interfere in the patient's treatment compliance [34]. Here HDI and GNI were negatively correlated with pre- and post-dialysis diastolic blood pressure, and positively correlated with pre- and post-dialysis weight and percentage of dialysis catheters. Modality choice, patient outcomes, and survival in dialysis population can be related to many health care resource indicators, such as pre-dialysis health care access, health care system sophistication (insurance or public health system coverage), number of healthcare personnel and facilities, social security support, and percentage of GDP spent on health care [35,36,37,38,39,40,41,42,43,44]. In this study, some negative correlations were found between blood pressures variables and health expenditure, and also between IDWG and percentage of physicians and nurses in the general population.

This study has several limitations. First, the MONDO database does not comprise all HD patients of a given country. Consequently, the correlational analysis is incomplete. Second, the absence of some biologically plausible associations may reflect the heterogeneity of this group (27 countries). Third, testing for multiple correlations may result in spurious correlations. Fourth, some data are missing from the MONDO data base, especially comorbidity data. Fifth, socioeconomic indicators were not available from all countries. Sixth, we did not have the data to compare country-specific general population and MONDO data during the same years. Nevertheless, we believe that our study has merits. For the first time, we were able to study the associations between the characteristics of prevalent HD patients and health and macroeconomic factors in the respective background population of 27 countries. We were able to explore 40 dialysis variables and 52 global health indicators for the presence or absence of associations in over 85,000 HD patients. The study provides a panoramic view of the interrelationship between socioeconomic factors, access to RRT, and patient comorbidities, and outcomes.

This hypothesis-generating study may stimulate future analysis of how global health indicators may interfere in RRT access, treatment provision, dialytic treatment characteristics, and outcomes.

RH and DY were fellows from the International Society of Nephrology (ISN) and received funding from the ISN for a Research Fellowship at Pontificia Universidade Católica do Paraná, Curitiba, Brazil during this manuscript elaboration. PK and LU hold stock in Fresenius Medical Care. RPF and VCS receive Scholarship from CNPq Brazilian Council for Research Support. The other authors have no financial interests to declare.

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