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Introduction
The treatment of end-stage chronic kidney disease (ESKD) is carried out through three general modalities of renal replacement therapies (RRT): peritoneal dialysis (PD), hemodialysis (HD), and kidney transplantation (KT). Due to its high cost, coupled with the increasing incidence of ESKD1, these treatments pose a significant challenge to the financial balance of health systems worldwide2. RRTs are crucial for patient survival and cannot be disregarded based on cost-effectiveness criteria alone, requiring universal and equitable access to be guaranteed3-5. However, this is particularly difficult in low- or limited-resource countries, where the primary need is the implementation of universal health coverage policies ethically grounded in equity and the rational use of resources6. On the other hand, the higher global prevalence of HD compared to other modalities2,7 suggests a supply defined by market logic rather than public health needs8-10.
In general terms, macroeconomic and social determinants play a fundamental role in people's health11, both in relation to variables such as wealth and development, and the level of spending allocated to health as a percentage of the gross domestic product (GDP). Scientific evidence has consistently shown that long-standing inequalities in kidney health have caused socially and economically disadvantaged groups to be at greater risk of poorer health outcomes12. The factors determining the supply and demand for health services in general and RRTs in particular are multiple and highly complex to address: some are directly related to technical-medical criteria, but factors related to the economic13,14, political, and cultural environment of each country or region, such as in Latin America15,16, also play a role. These include health policies, financing, infrastructure, human resources, access to medical supplies, and socioeconomic inequalities, among others.
The population of Latin America comprises nearly 630 million people (9% of the global population) with a life expectancy four years higher than the global average (76 vs. 72 years). It constitutes the most urbanized region in the world (80% of its population lives in cities) and allocates an average of 6% of GDP to health (lower than the 8.8% in countries of the Organization for Economic Cooperation and Development [OECD])17,18. Furthermore, as evidence of inequality, approximately 250 million people in Latin America lack social security (46% of the population), a figure19 similar to poverty levels (40.2%, with 11.2% in extreme poverty).
In this context, the aim is to analyze the potential impact and relationship of selected socioeconomic determinants on the supply of and access to RRTs in our region: the objective is to identify and objectively characterize possible sources of inequity to develop more specific and tailored health policies to improve the kidney health of the population, distributing resources efficiently, effectively, and accessibly across the different modalities.
Method
Relevant variables were selected to address the issue, and an adaptive methodology was chosen to estimate correlations among them, as described below.
Database
A preliminary database was developed, consisting of 37 socioeconomic, demographic, and health-related variables, with data from 19 Latin American countries. Subsequently, a selection process was carried out considering the effective and updated access to sources16,20-23 and their reliability level, resulting in a final list of 28 variables. The countries included were Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, the Dominican Republic, Uruguay, and Venezuela. To complete and update relevant information about the availability of RRT, a specific survey on access to nephrological care was conducted. It was answered by 98 nephrologists from the selected countries (average 5.2 responses per country).
Various grouping categories were defined for the final variables (Table 1). The analysis focused on the impact of determinants on access to and availability of RRT and was complemented with other supplementary correlations.
Table 1 Variables by category and information sources
| Variables | Source |
|---|---|
| Access | |
| Prevalence of RRT (PMP) | SLANH (RLADyT), 2019 |
| Incidence of RRT (PMP/year) | SLANH (RLADyT), 2019 |
| Prevalence of HD (PMP) | SLANH (RLADyT), 2019 |
| Prevalence of DP (PMP) | SLANH (RLADyT), 2019 |
| Prevalence of KT (PMP) | SLANH (RLADyT), 2019 |
| Supply | |
| Number of nephrologists (PMP) | SLANH (Enc.Acceso), 2024 |
| HD centers (PMP) | SLANH (Enc.Acceso), 2024 |
| PD centers (PMP) | SLANH (Enc.Acceso), 2024 |
| KT centers (PMP) | SLANH (Enc.Acceso), 2024 |
| Patients per HD center | Composite variable |
| Patients per DP center | Composite variable |
| Patients per KT center | Composite variable |
| Year of first HD program | SLANH (Enc.Acceso), 2024 |
| Year of first KT program | SLANH (Enc.Acceso), 2024 |
| Degree of advancement on KT law | SLANH (Enc.Acceso), 2024 |
| Demographics | |
| Population (inhabitants) | CEPAL, 2019 |
| Urban population (%) | CEPAL, 2019 |
| Population density (inhabitants/km2) | CEPAL, 2019 |
| Economics | |
| GDP per capita (USD) | OPS-WHO, 2019 |
| GDP per capita adjusted by PPP (USD) | OPS-WHO, 2019 |
| World Bank Classification | World Bank, 2019 |
| Gini Index | OPS-WHO, 2019 |
| Kuznets Ratio | OPS-WHO, 2019 |
| Poverty (%) | OPS-WHO, 2020 |
| Extreme poverty (%) | OPS-WHO, 2020 |
| Health System | |
| Total health spending (%) | OPS-WHO, 2019 |
| Private health spending (%) | OPS-WHO, 2019 |
| Public health spending (%) | OPS-WHO, 2019 |
| Out-of-pocket health spending (%) | OPS-WHO, 2015 |
| Social security coverage (% of population) | SLANH (Enc.Acceso), 2024 |
| Public health coverage (% of population) | SLANH (Enc.Acceso), 2024 |
CEPAL: Economic Commission for Latin America and the Caribbean; PD: peritoneal dialysis; Enc.: survey; HD: hemodialysis; OPS-WHO: Pan American Health Organization - World Health Organization; GDP: Gross Domestic Product; PMP: per million people; PPP: purchasing power parity; RLADyT: Latin American Dialysis and Transplant Registry; SLANH: Latin American Society of Nephrology and Hypertension; RRT: renal replacement therapy; KT: kidney transplant.
Data analysis
Pearson (r) and Spearman (ρ) coefficients were used to assess correlations between all variables. Statistical significance was defined in all cases as a p-value < 0.05. Variables showing statistically significant Pearson correlations with a coefficient greater than 0.60 were modeled using univariate linear regression. Residual normality was evaluated through visual inspection of residual plots against the independent variable. Analyses were performed using JASP and Microsoft Excel.
Results
Tables sequentially present the main correlations regarding access to and availability of services, along with other supplementary correlations. Figures, organized similarly, display linear regressions of the strongest correlations (r ≥ 0.60).
Access to renal replacement therapy services
Table 2 shows statistically significant correlations for access to RRT variables and the rest of the analyzed variables, while figure 1 presents linear regressions of the strongest associations (r ≥ 0.60).
Table 2 Correlations for the variables of access to RRT and other analyzed variables
| Variables | Global prevalence of RRT | Incidence of RRT | Prevalence of HD | Prevalence of PD | Prevalence of KT | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| r (p) | ρ (p) | r (p) | ρ (p) | r (p) | ρ (p) | r (p) | ρ (p) | r (p) | ρ (p) | |
| Prevalence of global RRT | - | - | 0.68 (0.002) | 0.50 (0.037) | - | - | - | - | - | - |
| Prevalence of HD | 0.76 (< 0.001) | 0.75 (< 0.001) | NS | NS | - | - | NS | NS | NS | NS |
| Prevalence of DP | 0.51 (0.025) | 0.47 (0.043) | NS | NS | NS | NS | - | - | - | - |
| Prevalence of KT | 0.82 (< 0.001) | 0.74 (< 0.001) | 0.81 (< 0.001) | 0.52 (0.028) | NS | NS | 0.59 (0.008) | 0.48 (0.041) | - | - |
| Number of nephrologists | NS | NS | NS | NS | 0.63 (0.006) | NS | NS | NS | NS | NS |
| HD units | NS | 0.50 (0.035) | NS | NS | 0.70 (0.001) | 0.67 (0.003) | NS | NS | NS | NS |
| PD units | 0.63 (0.005) | 0.63 (0.006) | 0.55 (0.023) | NS | NS | NS | 0.50 (0.035) | 0.52 (0.028) | 0.77 (< 0.001) | 0.84 (< 0.001) |
| KT units | 0.56 (0.016) | 0.57 (0.015) | NS | NS | 0.56 (0.016) | 0.63 (0.006) | NS | NS | 0.50 (0.033) | 0.54 (0.023) |
| Year of first HD program | –0.55 (0.017) | –0.52 (0.028) | NS | NS | –0.64 (0.004) | –0.68 (0.002) | NS | NS | NS | NS |
| Year of first KT program | –0.60 (0.009) | –0.53 (0.024) | NS | NS | –0.52 (0.026) | –0.57 (0.014) | NS | NS | –0.55 (0.017) | NS |
| Degree of advancement of KT law | 0.53 (0.023) | 0.59 (0.009) | NS | NS | 0.52 (0.028) | 0.63 (0.005) | NS | NS | 0.50 (0.045) | 0.54 (0.020) |
| Population | NS | NS | 0.48 (0.042) | NS | NS | NS | NS | NS | 0.53 (0.020) | NS |
| Urban population | 0.47 (0.044) | NS | NS | NS | NS | NS | NS | NS | 0.47 (0.044) | 0.46 (0.047) |
| Population density | NS | NS | NS | NS | NS | –0.50 (0.030) | 0.50 (0.031) | 0.49 (0.032) | NS | NS |
| GDP per capita | 0.58 (0.009) | 0.61 (0.007) | NS | NS | 0.50 (0.029) | 0.46 (0.049) | NS | NS | 0.57 (0.012) | 0.76 (< 0.001) |
| GDP per capita adjusted by PPP | 0.54 (0.025) | 0.62 (0.009) | NS | NS | NS | 0.50 (0.045) | NS | NS | 0.49 (0.044) | 0.76 (< 0.001) |
| World Bank Classification | 0.51 (0.026) | 0.51 (0.027) | NS | NS | 0.58 (0.010) | 0.52 (0.022) | NS | NS | NS | NS |
| Extreme poverty | NS | –0.50 (0.038) | NS | NS | NS | NS | NS | NS | NS | 0.54 (0.023) |
| Private health spending | NS | NS | NS | 0.52 (0.028) | NS | NS | NS | NS | NS | NS |
PD: peritoneal dialysis; HD: hemodialysis; NS: not significant; GDP: gross domestic product; PPP: purchasing power parity; RRT: renal replacement therapy; KT: kidney transplant.
Availability of renal replacement therapy services
Table 3 displays significant correlations for the availability of professionals and RRT services and the rest of the analyzed variables. Figures 2 to 5 show linear regressions for the availability of professionals and HD, PD, and KT services (measured as the number of centers and patients per center) in relation to the explanatory variables with the strongest associations.
Table 3 Correlations for the variables of the supply of professionals and RRT services and other analyzed variables
| Variables | Number of Nephrologists | HD units | PD units | KT units | ||||
|---|---|---|---|---|---|---|---|---|
| r (p) | ρ (p) | r (p) | ρ (p) | r (p) | ρ (p) | r (p) | ρ (p) | |
| HD units | 0.70 (0.001) | 0.76 (< 0.001) | - | - | NS | NS | 0.55 (0.018) | 0.53 (0.024) |
| KT units | 0.55 (0.018) | 0.55 (0.021) | 0.55 (0.018) | 0.53 (0.024) | 0.64 (0.004) | 0.61 (0.008) | - | - |
| Year of first HD program | NS | –0.56 (0.015) | NS | NS | NS | NS | NS | NS |
| Degree of advancement of KT law | NS | 0.48 (0.044) | NS | NS | 0.52 (0.027) | 0.52 (0.026) | 0.60 (0.009) | 0.58 (0.012) |
| Urban population | 0.62 (0.006) | 0.70 (0.002) | 0.64 (0.004) | 0.55 (0.019) | NS | NS | NS | NS |
| GDP per capita | 0.56 (0.016) | 0.50 (0.037) | 0.60 (0.009) | 0.52 (0.028) | 0.71 (< 0.001) | 0.75 (< 0.001) | 0.66 (0.003) | 0.61 (0.008) |
| GDP per capita adjusted by PPP | NS | NS | 0.53 (0.030) | 0.54 (0.028) | 0.77 (< 0.001) | 0.82 (< 0.001) | 0.58 (0.016) | 0.60 (0.013) |
| World bank classification | 0.51 (0.032) | 0.48 (0.044) | 0.56 (0.016) | 0.50 (0.034) | NS | NS | 0.48 (0.046) | NS |
| Gini index | NS | –0.48 (0.046) | –0.48 (0.043) | –0.47 (0.049) | NS | NS | NS | NS |
| Poverty | –0.60 (0.008) | –0.56 (0.016) | –0.61 (0.007) | –0.62 (0.006) | NS | NS | –0.49 (0.038) | –0.50 (0.034) |
| Extreme poverty | –0.62 (0.006) | –0.70 (0.002) | –0.70 (0.001) | –0.70 (0.002) | –0.57 (0.013) | –0.63 (0.006) | –0.61 (0.007) | –0.62 (0.007) |
| Total health spending | NS | NS | NS | 0.52 (0.028) | NS | NS | 0.50 (0.034) | NS |
| Public health spending | NS | NS | NS | NS | NS | 0.59 (0.011) | 0.52 (0.031) | - |
PD: peritoneal dialysis; HD: hemodialysis; NS: not significant; GDP: gross domestic product; PPP: purchasing power parity; RRT: renal replacement therapy; KT: kidney transplant.
Figure 2 Availability of professionals and relevant correlations. HD: hemodialysis; PMP: per million people.
Figure 4 Availability of DP and relevant correlations. PD: peritoneal dialysis; GDP: gross domestic product; PPP: purchasing power parity; PMP: per million people; KT: kidney transplant.
Figure 5 Availability of KT and relevant correlations. GDP: gross domestic product; PMP: per million people; RRT: renal replacement therapies; KT: kidney transplant.
The composite variable of patients per HD unit showed a direct correlation with the incidence of RRT, population, and poverty indices; the number of patients per PD unit showed similar positive relationships and a negative correlation with the number of nephrologists per country; finally, the variable of patients per kidney transplant unit showed a positive correlation with the prevalence and incidence of RRT, GDP per capita, and population size (Table 4).
Table 4 Correlations for the composite variables of RRT service supply and other analyzed variables
| Variables | Patients per HD unit | Patients per PD unit | Patients per KT unit | |||
|---|---|---|---|---|---|---|
| ρ (p) | r (p) | ρ (p) | r (p) | ρ (p) | r (p) | |
| Prevalence of global RRT | NS | NS | NS | NS | 0.75 (< 0.001) | 0.74 (< 0.001) |
| Incidence of RRT | 0.50 (0.042) | NS | NS | NS | 0.76 (< 0.001) | 0.50 (0.045) |
| Number of nephrologists | NS | NS | NS | –0.56 (0.018) | NS | NS |
| Population | NS | 0.48 (0.045) | NS | NS | 0.59 (0.011) | NS |
| Population density | NS | NS | 0.57 (0.013) | 0.77 (<0.001) | NS | NS |
| GDP per capita | NS | NS | NS | NS | NS | 0.53 (0.030) |
| GDP per capita adjusted by PPP | NS | NS | NS | NS | NS | 0.52 (0.035) |
| Kuznets ratio | 0.58 (0.036) | 0.59 (0.036) | NS | NS | NS | NS |
| Poverty | 0.48 (0.044) | NS | 0.55 (0.019) | NS | NS | NS |
| Extreme poverty | 0.49 (0.041) | 0.57 (0.015) | NS | NS | NS | NS |
PD: peritoneal dialysis; HD: hemodialysis; NS: not significant; GDP: gross domestic product; PPP: purchasing power parity; RRT: renal replacement therapy; KT: kidney transplant.
Other relevant correlations
To ensure model consistency and thorough analysis, complementary correlations among explanatory variables were studied, revealing significant results (Table 5).
Table 5 Correlations for complementary variables and other analyzed variables
| Variables | Population | Urban population | Population density | |||
|---|---|---|---|---|---|---|
| ρ (p) | r (p) | ρ (p) | r (p) | ρ (p) | r (p) | |
| First HD program year | –0.48 (0.043) | –0.54 (0.022) | –0.74 (< 0.001) | –0.71 (0.001) | 0.52 (0.028) | 0.61 (0.007) |
| First KT program year | –0.49 (0.041) | –0.63 (0.006) | –0.75 (< 0.001) | –0.74 (< 0.001) | NS | NS |
| GDP per capita | NS | NS | 0.64 (0.003) | 0.66 (0.003) | NS | NS |
| GDP per capita adjusted by PPP | NS | NS | 0.61 (0.009) | 0.67 (0.004) | NS | NS |
| World bank classification | NS | NS | 0.52 (0.022) | 0.48 (0.037) | NS | NS |
| Gini index | 0.47 (0.048) | NS | NS | NS | NS | NS |
| Kuznets ratio | 0.64 (0.018) | NS | NS | NS | NS | NS |
| Poverty | NS | NS | –0.73 (< 0.001) | 0.62 (0.006) | NS | NS |
| Extreme poverty | NS | NS | –0.72 (< 0.001) | –0.75 (< 0.001) | NS | NS |
| Out-of-pocket health spending | NS | NS | –0.66 (0.003) | –0.69 (0.006) | NS | 0.54 (0.020) |
| Social security coverage | NS | NS | 0.54 (0.021) | 0.55 (0.018) | NS | NS |
| Variables | Poverty | Extreme poverty | ||
|---|---|---|---|---|
| ρ (p) | r (p) | ρ (p) | r (p) | |
| First HD program year | 0.52 (0.029) | 0.49 (0.038) | 0.51 (0.032) | 0.52 (0.028) |
| Kuznets ratio | –0.73 (< 0.001) | –0.56 (0.016) | –0.65 (0.003) | NS |
| GDP per capita | –0.74 (< 0.001) | –0.75 (< 0.001) | –0.73 (< 0.001) | –0.75 (< 0.001) |
| GDP per capita adjusted by PPP | –0.69 (0.002) | –0.72 (0.002) | –0.67 (0.004) | –0.68 (0.003) |
| World bank classification | –0.70 (0.001) | –0.74 (< 0.001) | –0.63 (0.005) | –0.60 (0.009) |
| Out-of-pocket health spending | 0.59 (0.011) | NS | 0.48 (0.042) | NS |
| Social security coverage | –0.50 (0.034) | –0.54 (0.022) | NS | –0.49 (0.041) |
| Variables | Total health spending (r) | Private health spending | Public health spending | Out-of-pocket health spending | ||||
|---|---|---|---|---|---|---|---|---|
| ρ (p) | r (p) | ρ (p) | r (p) | ρ (p) | r (p) | ρ (p) | r (p) | |
| Progress of KT law | NS | NS | NS | NS | NS | NS | –0.60 (0.009) | –0.54 (0.021) |
| Social security coverage | NS | NS | –0.47 (0.049) | NS | NS | NS | NS | NS |
| Variables | Population with social security coverage | Population with public health coverage (ρ) | ||
|---|---|---|---|---|
| ρ (p) | r (p) | ρ (p) | r (p) | |
| Progress of KT law | 0.55 (0.017) | 0.62 (0.006) | NS | NS |
| GDP per capita | 0.62 (0.006) | 0.68 (0.002) | NS | NS |
| GDP per capita adjusted by PPP | 0.73 (< 0.001) | 0.79 (< 0.001) | NS | NS |
| World bank classification | 0.54 (0.020) | 0.59 (0.010) | NS | NS |
PD: peritoneal dialysis; HD: hemodialysis; NS: not significant; GDP: gross domestic product; PPA: purchasing power parity (PPP); TRR: renal replacement therapy; KT: kidney transplant.
The population per country showed a negative correlation with the year of the first HD and KT program. Urban population was positively related to wealth indicators; higher urban population and density were associated with greater advancement in KT legislation, earlier initiation of RRT programs, and higher social security coverage. Poverty and extreme poverty were correlated with out-of-pocket health spending, while a negative relationship was observed with the advancement of KT legislation.
Discussion
Although over the past decade the financial burden posed by ESRD and RRT has gained traction in nephrology publications and academic forums, discussions continue to address general terms with limited impact on decisions toward more rational resource allocation1,2,24,25. From a non-financial but economic perspective, as with other high-cost health interventions, resource allocation does not follow public health principles but reflects the incentives of the technological market8,10. In this context, Nephrology despite the complexity of managing terminal patients, initially expanded through the promotion of HD as a cost-effective method, later developing standardization and quality systems that optimized its benefits, creating large global service provision markets26. However, given increasingly scarce healthcare resources, this strategy seems to have encountered limitations in clinical outcomes, restricting access to more cost-effective and less prevalent therapies such as KT (mainly due to donor shortages)26 and PD (often resigned as a complementary alternative due to minimal incentives)27,28. Moreover, in health systems striving for universal coverage, the ethical imperative of rational resource use to improve equity5,6 and patient-centered strategies26 conflicts with concepts of technological adoption and financial profitability. It is within this context that the present study sought to investigate the characteristics of RRT availability and access from a socioeconomic perspective, highlighting the unequal scenarios in Latin America. The results for each category of selected variables are analyzed sequentially below.
Access to renal replacement therapy services
Strong correlations are observed between the greater global prevalence of patients undergoing RRT in countries with more mature and comprehensive treatment modalities, including HD, PD, and KT units, determined by the year programs began and legislative advances in KT. There is also a positive relationship with higher urban concentration and economic development indicators (GDP per capita and World Bank classification). Conversely, there is a negative relationship with extreme poverty levels, pointing to potential inequities in access. These findings support a trend of access to high-cost, complex therapies being concentrated in more economically developed areas3,7,10,24 Early establishment of the Nephrology as a special field allowed these countries to later provide treatments such as KT26, which represent an evolution in healthcare and social requirements formalized through procurement legislation25.
Breaking down by modality, the prevalence of HD patients correlated with the year RRT programs began (both HD and KT: the earlier they started, the greater the current availability and prevalence), the number of nephrologists and treatment units, and GDP per capita. These elements are essential for the spread of this therapeutic option. The availability of PD units directly correlates with the prevalence of PD patients and higher population density. Finally, the prevalence of KT patients is directly related to variables such as urban concentration, total population, national wealth, and the prevalence and incidence of RRT patients24,25 as well as the availability of treatment centers. This suggests that KT complements overall RRT access but requires a prior foundation of economic development and consolidated training. In this subanalysis of each modality, HD and KT reinforce correlations with variables similar to those observed for global prevalence, while PD is associated with specific availability conditions cited in the literature2,3,5,15.
Regarding the incidence of RRT patients, correlations align with literature reports, showing a positive association with population size1,24,25 and private health spending. This could have two explanations: higher incidence may reflect access to differential care (assuming higher quality and access due to direct payment) or indicate that this higher out-of-pocket spending reflects issues in public health systems' capacity to prevent ESRD progression effectively6,12,24.
Offer of renal replacement therapy services
The unequal distribution of nephrologists highlights a significant current and future issue for the region1,7,13,16: their higher concentration is clearly observed in countries with greater wealth (and lower poverty rates)16, larger urban populations, and greater income equality. Furthermore, a greater number of nephrologists is also associated with a longer history of the specialty, using the establishment of the first HD program as a proxy. This, in turn, suggests a greater initial availability of HD in terms of prevalence and centers2,26. A similar interpretation can be made regarding the direct correlation between a higher number of nephrologists and the existence of more KT units. These findings reinforce access correlations: the early introduction of nephrology in higher-income countries and the early initiation of programs fostered the initial expansion of HD10,26, likely influenced by the dissemination of technologies in emerging and economically solvent markets24,25.
Similarly, the explicit availability of HD centers correlates directly with wealthier countries, urban concentration, income equality (and lower poverty), and the aforementioned prevalence of RRT and HD, alongside the coexistence of more KT units (possibly as a result of the established primary dialysis options)26. Adjusting the number of centers by prevalence reveals a previously unexplored relationship in the literature: countries with centers serving more concentrated patient populations also exhibit greater inequalities and poverty. This finding is notable, potentially linking larger facility sizes to countries with fewer specialists available to provide care.
A similar pattern is observed with PD centers: although their number per country correlates with higher economic development, lower poverty, and greater RRT prevalence, when adjusted for patient concentration (more patients per unit), a positive correlation emerges with higher poverty levels. Finally, in terms of KT units per million inhabitants, their presence is consolidated in wealthier countries with higher healthcare spending, where RRT prevalence is greater, service offerings are more developed26, and legislative advancements on KT are more defined29. However, the association with financing mechanisms is also remarkable, predominating in countries with a higher public healthcare share, indicating equitable policies for access to high-complexity interventions. Additionally, when considering the proportion of patients per unit, higher volumes are observed in countries with larger populations and earlier transplant program initiation, suggesting greater expertise and centralized management of larger patient cohorts under follow-up.
Other relevant correlations
Further exploration of correlations to confirm the logical behavior of selected variables in the model revealed expected results. Larger country populations inversely correlated with the timing of the first RRT programs (the larger the population, the earlier the dialysis and KT programs began) or directly with income equality (though this correlation may have more complex explanations)30. Beyond the previously mentioned relationships with treatment prevalence and units availability, greater urban density directly correlated with wealthier countries, lower poverty, and broader social security coverage30,31. Inverse correlations were observed with the timing of RRT program initiation (more urban populations saw earlier therapy supply, consolidating the idea of greater service availability stemming from urban centers) and out-of-pocket healthcare spending (greater population concentration correlates with lower out-of-pocket costs, perhaps reflecting stronger economic development and formal employment in urban areas)30,32. Supporting this, indicators of healthcare spending and coverage showed that a greater proportion of people covered by social security directly correlated with wealth indices and inversely with out-of-pocket spending. Complementarily, a direct relationship was found between out-of-pocket spending and poverty-related economic indicators (healthcare access depends on the individual economies of patients and their families) but was clearly inverse with the proportion of people covered by public systems. This suggests a necessary compensatory effect from public health to ensure equity in healthcare access30,31. To mitigate the direct impact of limited accessibility, addressing this imbalance in coverage capabilities could become an area of development for alternative insurance models for catastrophic illnesses.
Limitations
The sources of information correspond to records that, in some cases, have been validated and published, originating from official statistics and supplemented with preliminary data from ad hoc records and surveys currently being updated and published. Even in this context, data closest to the period prior to the COVID-19 pandemic were adjusted for proper comparison. The pandemic disrupted socioeconomic indicators in these countries as well as the availability of RRT services33.
While there is significant effort by scientific societies to consolidate RRT registries, these efforts should be strengthened by considering the socioeconomic and historical context in which they are implemented. Including determinants like those analyzed here could improve the perspective and accuracy of such registries. These results are general and indicative, and they do not fully explain specific exceptional situations, such as the noteworthy case of Costa Rica's predominance of KT prevalence4, which represents a unique case study with unique dynamics.
Finally, the limited number of countries in the region poses a sampling limitation, preventing more comprehensive statistical analyses through multivariate methodologies. Although correlations indicating trends in the behavior of the selected variables were established, they do not imply causality or direct association, nor do they determine the relative weight of the most sensitive variables compared to the accessory ones beyond the scope of evaluation. Nonetheless, the approximate values derived from combining methods could indicate associations for future lines of research on this topic.
Conclusions
In general, it is observed that access variables (incidence, overall prevalence, and RRT-specific prevalence) correlate with the complete availability of modalities, early program initiation, advancements in procurement legislation, greater urban concentration, and economic development indicators. On the other hand, the availability of professionals and services is unequal, with higher concentrations in wealthier countries, earlier Nephrology program initiation, greater urban population concentration, and income equality, leading to a higher prevalence of HD units compared to PD and KT. Healthcare coverage in the region is characterized by out-of-pocket expenses and their complementarity with public health as predominant in countries with higher poverty rates, while social security coverage in those with larger populations, greater urban concentration, and higher wealth. In this financing framework, efforts should focus on promoting cost-effective RRT options to improve equitable access6.
Understanding this process of RRT implementation through an economic and historical lens could provide a realistic foundation for future discussions on improving equity in resource distribution and its impact on health policies in Latin America. The findings from this analysis are unprecedented for the region, as there are few bibliographic references on the subject. This underscores the importance of this work, not for establishing definitive explanations of direct associations but to understand the distribution logic of patient care, the sources of inequity, and to open new pathways for research on decision-making and resource allocation in healthcare.
