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INTRODUCTION
In Brazil, Law No. 11,445, of January 5, 2007, establishes the national guidelines for basic sanitation, defined as a set of public services, infrastructure, and operational facilities for the supply of potable water, sewage disposal, urban cleaning, solid waste management, drainage, and management of urban rainwater1. This means that sanitation is a set of measures aimed at preventing diseases, promoting health, and improving quality of life through the preservation and modification of environmental conditions2.
Approximately 80% of the water used for human consumption becomes domestic sewage3, the Instituto Trata Brasil2 highlights that inadequate disposal can be a vehicle for pathogenic germs and various diseases, as well as making the water in rivers and lakes that receive this type of effluent unsuitable for human consumption. In the list that makes up the health indicators, infant mortality (in children under one year old) is linked to access to and consumption of potable water, since high rates of this indicator expose precarious living and health conditions⁴ .
For comparison, in Brazil the lowest mortality rate occurs in the South region, with 10.2 deaths/1,000 live births, while in European countries the rate is around 3 deaths/1,000 live births . Untreated sewage is one of the main forms of water pollution in Brazil5; according to the Brazilian Business Council for Sustainable Development (CEBDS), the annual average of hospitalizations due to water-related diseases is 128,000, causing approximately 1,500 deaths per year. In regions with low access to water and sewage services, hospitalization rates are even higher.
Given the importance of water quality for health, the Environmental Company of the State of São Paulo (CETESB) adapted and developed the Water Quality Index (WQI) based on a 1970 study by the National Sanitation Foundation of the United States. The index consists of nine variables considered essential for assessing water quality, mainly for public supply purposes6. According to Toledo et al.7, basic sanitation indicators and the WQI are related, proving that the lack of an adequate basic sanitation system directly influences the degradation of water quality in water bodies.
Despite the legislative framework covering the sector, deficits in the basic sanitation sector in Brazil are historical. Even with the creation of plans and the establishment of laws, over the years the resources allocated to the services have proven insufficient, since, according to Turolla8 , the sanitation sector is characterized by high fixed costs and highly specific capital.
The lack of investment results in operational and financial inefficiency, directly impacting the quality of services provided. According to Bittelbrunn , Brinckmann, Andrett, and Pfitscher9, the main deficits in the sector lie in sewage collection and treatment services, affecting water quality and consequently negatively influencing the health and quality of life of the population10. This is mainly due to the problem of mortality associated with infectious and preventable diseases, such as recurrent episodes of diarrhea in children and maternal conditions that contribute to perinatal deaths11. These deficiencies are increasing in less economically developed areas, which have greater needs regarding access to potable water12.
Previous studies have addressed similar themes; Silva and Esperidião13 analyzed the impact of sanitation service coverage on infant mortality and economic development, specifically in the Brazilian Northeast. This approach is novel because it explores variables in an integrated way, allowing the identification of patterns and structural inequalities across the Brazilian territory.
Recognizing the importance of basic sanitation and water quality for health, especially in the context of preventable infectious diseases, as well as their impacts on education, the regional economy, and human development, it becomes essential to understand how the lack of adequate investment in these areas affects the well-being of the population.
Given this scenario, this study seeks to investigate how expenditures on basic sanitation services correlate with water quality indicators in Brasil. Thus, the objective is to avaluate the relationship between investment in sanitation and water quality, as well its effects on the Municipal Human Development Index.
METHODS
Study Design
This is an ecological study in Brazilian macro-regions, seeking to understand the relationship between operating expenses for basic sanitation services, representing a measure of public investment effort, and water quality indicators: (i) - incidence of turbidity analyses outside the standard; and (ii) - incidence of total coliform analyses outside the standard, which reflect the efficiency of the services provided, in addition to analyzing how these impact the Municipal Human Development Index (MHDI), used as a parameter for the human development of the Brazilian population.
Study Location and Period
The study considered the Brazilian territory, grouped by macro-regions. The data used refers to the year 2021, the last period with consolidated information from all the data used.
Study Population and Eligibility Criteria
The study population comprises the federative units and all Brazilian municipalities available in the public databases used. The variables operating expenses for basic sanitation services and the Municipal Human Development Index (MHDI) considered the resident population of the analyzed municipalities, while the variables considered as water quality indicators refer to the municipalities themselves. Municipalities with missing or inconsistent data were adjusted to avoid compromising the analyses.
Data Collection
The data were collected from public databases, specifically the National Sanitation Information System (SNIS) and the Atlas of Human Development in Brazil, both made available by the Federal Government.
From the SNIS (National Sanitation Information System), the variable “operating expenses with basic sanitation services” was extracted by federative unit (UF), in addition to the variables that make up the water quality indicators, for which data are available from all municipal analysis points in the Brazilian territory. The HDI (Human Development Index) variable, provided by UF, was extracted from the Atlas Brasil de Desenvolvimento Humano (Human Development Index of Brazil).
In the present study, the intention was to use the Water Quality Index (WQI) as an indicator of water quality, since the WQI incorporates nine essential variables to assess water quality into a single value, including the parameters: thermotolerant coliforms, pH, biochemical oxygen demand, total nitrogen, total phosphorus, temperature, turbidity, total sodium, and dissolved oxygen6.
However, the tool was not used due to difficulties related to the inconsistency and discrepancy of the data obtained from public databases. Using only the indicators included in the WQI: (i) – Number of occurrences, incidence, of turbidity analyses outside the standard; and (ii) - incidence of total coliform analyses outside the standard. The index is calculated using the weighted average of these parameters, each having a relative weight corresponding to its level of importance, the result is a value from 0 to 100. The classification of water can be from very poor to excellent, with WQI < 20 classified as very poor and WQI > 79 classified as excellent .
The National Water Agency (ANA) was responsible for launching the National Water Quality Assessment Program (PNQA)14. The program aims to disseminate knowledge about the quality of surface waters in Brazil, with a view to the sustainable management of water resources in the country. This dissemination occurs through the Water Quality Portal . According to information from the portal, thermotolerant coliforms are bacteria that indicate a source of pollution from domestic sewage, since they are present in human feces. Furthermore, the inadequate disposal of sewage and industrial effluents is responsible for increased turbidity in water bodies; in other words, both are related to the discharge of sewage into water bodies.
Data Analysis
The data were downloaded in CSV and XLSX spreadsheets, then processed, organized, and analyzed using RStudio software, version 2024.09.0+375. From the variables, a database was constructed with all municipal analysis points of water quality indicators, including data on operating expenses for basic sanitation services and the Municipal Human Development Index (MHDI).
In this study, we were interested in events related to expenses for basic sanitation services, water quality indicators, and the Municipal Human Development Index. Distinct analyses were carried out, divided into two models:
This model assesses the relationship between expenses on basic sanitation services (independent variable) and water quality indicators (dependent variable).
In this model, water quality indicators (independent variables) are analyzed in relation to the HDI (dependent variable).
In the statistical analysis, the correlation test was applied to both models. To analyze the correlation between sanitation expenses and water quality indicators, as well as between these indicators and the Municipal Human Development Index (MHDI), Pearson’s correlation coefficient was considered. For the comparison of means between Brazilian macro-regions (independent variable) in relation to sanitation operating expenses, water quality indicators, and MHDI (dependent variables), analysis of variance (ANOVA) was used.
The verification of the assumptions of the Pearson correlation test for the application of parametric statistics was carried out using the Kolmogorov-Smirnov test, recommended for large samples (n > 30), since other tests have lower precision in this context15. The test results indicated that none of the analyzed variables follows a normal distribution (p < 0.001). Therefore, Spearman’s correlation coefficient was chosen, a non-parametric statistical alternative to the Pearson correlation test.
The assumptions for using analysis of variance (ANOVA) were also verified, which requires that certain assumptions be met to ensure the accuracy of the results. Thus, the Kolmogorov-Smirnov test was used to verify the assumption of normality of the residuals; the results showed that none of the variables had residuals with a normal distribution (p < 0.001).
Furthermore, the assumption of homogeneity of variance, also known as homoscedasticity, one of the essential premises for performing analysis of variance (ANOVA), was verified. To test this assumption, the Breusch-Pagan test was used, which is more suitable for medium to large samples, becoming more effective as the sample size increases16. The test result indicated that none of the variables studied are homogeneous (p < 0.005). Thus, for the analysis of variance, the Kruskal-Wallis test was used, given that the assumptions of normality of residuals and homoscedasticity were violated.
To compare the regions and determine which ones show statistically significant differences, Dunn’s test was used. This test complements the results of the Kruskal-Wallis test, since Dunn’s test is a multiple comparisons test that compares all groups with each other; this analysis is also known as a post-hoc test. The significance level adopted in all the analyses mentioned was 5%.
Ethical and Legal Aspects of the Research
Because this study is based on secondary data available in public databases, without individual identification and openly accessible, there was no need to submit it to the Research Ethics Committee. All data used comply with public access and government transparency standards, in accordance with current Brazilian legislation.
RESULTS
Table 1 presents the averages related to operating expenses for basic sanitation services, incidence of non-standard turbidity analyses, incidence of non-standard total coliform analyses, and the Municipal Human Development Index (MHDI) of the macro-regions studied, in addition to the representativeness of each region in the database. It can be seen that the regions with the highest number of analysis points for water quality indicators are the Southeast and Northeast regions, with 39.26% and 27.94%, respectively.
Table 1 : Descriptive statistics: means of variables by Brazilian macro-region - Brazil, year 2021.
| Descriptive measurements | Central-West | North East | North | Southeast | South |
|---|---|---|---|---|---|
| Operating expenses for basic sanitation services (R$/year) | 1,748,110,255.00 | 1,225,893,494.00 | 223,510,532.00 | 3,637,326,125.00 | 2,330,658,826.00 |
| Incidence of turbidity analyses outside the standard (%) | 3.68 | 7.71 | 6.43 | 2.22 | 1.33 |
| Incidence of out-of-standard total coliform analyses (%) | 1.92 | 12.25 | 6.78 | 2.01 | 2.81 |
| Municipal Human Development Index (MHDI) | 0.74 | 0.70 | 0.71 | 0.78 | 0.78 |
| Sample size | 326 | 1794 | 589 | 2521 | 1191 |
Source : Author’s own work, 2025.
Regarding the other variables, the discrepancy between the North region and the other regions stands out in terms of operating expenses for basic sanitation services, remaining below half a billion reais. As for water quality indicators, the Northeast and North regions show the worst results, with higher incidence rates of turbidity and total coliform analyses outside the standard, highlighting that the Northeast presents double the incidence rate of total coliform analyses outside the standard compared to the other regions. The HDI (Human Development Index) shows small differences between the macro-regions, not reaching one-tenth of a difference; it is worth noting that the South and Southeast have the same value, 0.78.
For comparison, regarding operating expenses for basic sanitation services, the region with the highest operating expenses for these services in 2021 is the Southeast, with R$ 3,637,326,125.00, while the region with the lowest expenditure is the North, with only R$ 223,510,532.00 (Table 1).
Regarding water quality indicators, in the case of the incidence of turbidity analyses outside the standard, the Northeast region presents the highest percentage with 7.71%, while the South region has the lowest percentage with 1.33%. In relation to the incidence of total coliform analyses outside the standard, again the Northeast region presents the highest percentage with 12.25%, while the Central-West region has the lowest percentage with 1.92% (Table 1).
Regarding the Municipal Human Development Index (MHDI), the South and Southeast regions have the best indices, both with 0.78, while the lowest index occurs in the Northeast region, which has a value of 0.70 (Table 1).
Given that the assumptions for using Pearson’s correlation test were refuted, the analysis of the relationship between expenses on basic sanitation services and the variables incidence of non-standard turbidity analyses and incidence of non-standard total coliform analyses, and between these indicators and the Municipal Human Development Index (MHDI), was performed using Spearman’s correlation test.
Table 2 shows that there is a significant and inversely proportional association between operating expenses for basic sanitation services and the variables incidence of out-of-standard turbidity analyses and incidence of out-of-standard total coliform analyses, that is, as operating expenses for basic sanitation services increase, the incidence of out-of-standard turbidity (rho = -0.119, p < 0.001) and the incidence of out-of-standard total coliform analyses (rho = -0.186, p < 0.001) decrease.
Table 2 : Correlation analysis between the variable expenses with basic sanitation, water quality and Municipal Human Development Index (MHDI).
| Independent variables | Dependent variables | Spearman’s correlation coefficient (rho) | P |
|---|---|---|---|
| Operating expenses for basic sanitation services | Incidence of turbidity analyses outside the standard range. | -0.119 | p < 0.001 |
| Incidence of out-of-standard total coliform analyses. | -0.186 | p < 0.001 | |
| Incidence of turbidity analyses outside the standard range. | Municipal Human Development Index (MHDI) | -0.187 | p < 0.001 |
| Incidence of out-of-standard total coliform analyses. | -0.278 | p < 0.001 |
Source : Author’s own work, 2025.
According to the results in Table 2, the variables incidence of non-standard turbidity analyses and incidence of non-standard total coliform analyses show a significant and inversely proportional association with the Municipal Human Development Index. Thus, the lower the incidence of non-standard turbidity analyses, the higher the Municipal Human Development Index (rho = -0.187, p < 0.001). Likewise, the lower the incidence of non-standard total coliforms, the higher the Municipal Human Development Index (rho = -0.278, p < 0.001).
The study also analyzed whether there are significant differences between the group means regarding operating expenses for sanitation, water quality indicators, and the Municipal Human Development Index (MHDI) in the Brazilian macro-regions using the Kruskal-Wallis test, given that the assumptions for using the ANOVA test were refuted. To investigate the differences between the regions, a multiple comparison or post-hoc test was performed using Dunn’s test.
Table 3 shows that at least one of the Brazilian administrative regions has a different average from the others in relation to the variables analyzed.
Table 3 : Kruskal’s test for analysis of variance among Brazilian administrative regions.
| Kruskal-Wallis test | |||
|---|---|---|---|
| Variables | |||
| Brazilian administrative regions | Chi-square coefficient | Degrees of freedom (df) | P |
| Operating expenses for basic sanitation services | 1434.8 | 4 | p < 0.001 |
| Incidence of turbidity analyses outside the standard range. | 689.53 | 4 | p < 0.001 |
| Incidence of out-of-standard total coliform analyses. | 621.04 | 4 | p < 0.001 |
| Municipal Human Development Index (MHDI) | 5235 | 4 | p < 0.001 |
Source : Author’s own work, 2025.
According to the results in Table 4, regarding the variable of operating expenses for basic sanitation services, only the Southeast and South regions have similar averages. Regarding the incidence of turbidity analyses outside the standard, none of the regions are similar to each other; however, regarding the incidence of total coliform analyses outside the standard, the Northeast and North, Southeast and South regions showed similarities. Regarding the Municipal Human Development Index (MHDI), no region presented similar averages.
Table 4 : Dunn’s post-hoc multiple comparison test between Brazilian administrative regions.
| Dunn’s Test | ||||||||
|---|---|---|---|---|---|---|---|---|
| Variables | Operating expenses for basic sanitation services | Incidence of turbidity analyses outside the standard range | Incidence of out-of-standard total coliform analyses | Municipal Human Development Index (MHDI) | ||||
| Regions | Z | P.adj | Z | P.adj | Z | P.adj | Z | P.adj |
| Central-West – Northeast | 3.1278895 | p = 0.003 | 5.718076 | p < 0.001 | -4.027426 | p < 0.001 | 13.214245 | p < 0.001 |
| Central-West – North | 16.6716396 | p < 0.001 | 8.544906 | p < 0.001 | -4.499836 | p < 0.001 | 8.555079 | p < 0.001 |
| Northeast - North | 20.2686569 | p < 0.001 | 5.171604 | p < 0.001 | -1.435035 | P = 0.302 | -4.317339 | p < 0.001 |
| Central-West – Southeast | -7.0941344 | p < 0.001 | 14.252976 | p < 0.001 | 6.887995 | p < 0.001 | -21.702943 | p < 0.001 |
| Northeast – Southeast | -19.6146382 | p < 0.001 | 16,013203 | p < 0.001 | 20.975213 | p < 0.001 | -67.111785 | p < 0.001 |
| North – Southeast | -34.2705130 | p < 0.001 | 5.441348 | p < 0.001 | 15.645817 | p < 0.001 | -40.815587 | p < 0.001 |
| Central-West – South | -6.4410155 | p < 0.001 | 18.487608 | p < 0.001 | 5.702329 | p < 0.001 | -11.309922 | p < 0.001 |
| Northeast – South | -15.8099484 | p < 0.001 | 21.706754 | p < 0.001 | 16.023594 | p < 0.001 | -40,199534 | p < 0.001 |
| North – South | -30.8393909 | p < 0.001 | 11.231054 | p < 0.001 | 13.242514 | p < 0.001 | -25.758203 | p < 0.001 |
| Southeast - South | 0.4246687 | p = 0.671 | 9.007565 | p < 0.001 | -1.392793 | p = 0.164 | 16.223151 | p < 0.001 |
Source : Author’s own work, 2025.
DISCUSSION
This study analyzed the correlation between expenses on basic sanitation services and water quality indicators, and how these impact the Municipal Human Development Index (MHDI). It was found that the higher the spending on basic sanitation, the better the water quality indicators and, consequently, the MHDI also increases. This relationship is more pronounced when related to the incidence of total coliform analyses outside the standard range. The study also examined the differences in the variables studied across Brazilian macro-regions, noting that most regions differ from each other, although some share similarities in certain aspects.
Comparing the Brazilian macro-regions reveals a discrepancy in operating expenses for basic sanitation services. While the North region has average annual expenditures of approximately R$223 million, in the Southeast, the value exceeds R$3 billion. Regarding water quality indicators, the Northeast region presents the worst water quality indicators. The South and Central-West regions have the best indicators for the incidence of turbidity and total coliform analyses outside the standard, respectively. The HDI (Human Development Index) differs significantly between regions, with the lowest value found in the Northeast, at 0.70, and the highest in the South and Southeast regions, at 0.78.
Correlation analyses between sanitation expenditures and water indicators, as well as these in relation to the Human Development Index (HDI), resulted in a negative correlation. This means that higher investments are associated with a reduction in the occurrence of turbidity and total coliform counts outside the standard range, and lower levels are associated with higher HDI scores. Kruskal-Dunn tests showed that the Southeast and South regions have similar sanitation expenditures, as well as similar levels of total coliforms outside the standard range. Regarding the HDI, the North and Northeast regions have the worst indices, close to the average level, while the Southeast and South have the highest indices, close to the very high level, highlighting regional inequalities.
Historically, regional disparities in the basic sanitation sector have been shaped by several factors, mainly economic. The distribution of investments in the sector has been uneven across the country since the National Sanitation Plan (Planasa), created in 1971, as the plan was based on an economic bias and prioritized urban centers. The plan was one of the major milestones that boosted the development of sanitation services in Brazil17, as it was effective in expanding the coverage of sanitation services at the time8.
Nevertheless, Costa18 points out that the distribution of the plan’s resources did not take into account the criteria of social justice and equity, a fact evidenced by the large regional concentration, since the Southeast region received approximately 62.3% of the plan’s resources between 1968 and 1984, while the Northeast region received only 21.3%. When considering population weights, the Southeast region, with 44% of the population, received a larger share of the resources than its population proportion, while the Northeast region, with 29% of the population, received a smaller portion than its population weight, highlighting the discriminatory distribution when considering the relative weight of the population at the time.
In 2014, the National Basic Sanitation Plan (PLANSAB) was launched, which set goals for the universalization of basic sanitation services by 2033. It projected an investment need of R$ 435 billion for the universalization of water and sewage services between 2014 and 2033, equivalent to R$ 22 billion per year. Even with the investment targets foreseen in the plan, the amounts disbursed were far below what was needed between 2014 and the year studied in this research. In 2021, approximately R$ 4.1 billion was disbursed, an amount that corresponds to only 46.3% of the total investment projected for that year .
The inequalities in spending on basic sanitation services among the Brazilian macro-regions highlight the limited financial resources and the effects of prioritizing large urban centers. Analysis of operating expenses for sanitation services demonstrates that the distribution of investments does not proportionally reflect the population size across Brazilian regions. In populous regions like the Northeast, infrastructure remains precarious, evidencing the insufficient resources allocated to the sector.
While the Southeast region concentrates the highest spending on basic sanitation services, the Northeast and North regions have the lowest investments in the sector. This scarcity of resources contributes to the low coverage rates of sanitation services in certain regions, as Fernando points out19 , highlighting that the North and Northeast regions have the lowest rates in water supply, ranging between 54.5 and 70.4%, while the South and Southeast have the highest rates, both above 90%. Disparities in access to sanitation services in Brazil are caused by a lack of public investment, poverty, and regional inequalities.
Fernando et al.19 further emphasizes that the insufficient coverage of basic sanitation services generates negative externalities that compromise the quality of life of the population in less favored regions. Complementing this analysis, Rodrigues et al.10adds that these regions present higher cases of hospitalization due to waterborne diseases, in addition to presenting lower social indicators compared to the others. This reality is reflected in the HDI, with the North and Northeast regions presenting the lowest indices, while the Southeast and South present the highest indices.
The scarcity of financial resources is evident in structural and social aspects of the basic sanitation sector, also resulting in the inefficiency of water and sewage services, which are essential for water quality and the population’s quality of life10. The results of this study demonstrate that the higher the spending on basic sanitation services, the better the water quality indicators will be and, in turn, as the water quality indicators improve, the Municipal Human Development Index (MHDI) will consequently be better.
The study by Toledo et al.7also demonstrates that basic sanitation indicators and water quality indicators are related, proving that the lack of an adequate basic sanitation system directly influences the degradation of water quality in water bodies. Silva and Esperidião13show that basic sanitation services, specifically water supply and sewage disposal, are also directly related to infant mortality, an indicator of quality of life.
Regarding water quality indicators, the Northeast region presents the worst results in both variables, followed by the North region. In contrast, the South region has the best result in the incidence of out-of-standard turbidity analyses, and the Central-West stands out in the incidence of out-of-standard total coliform analyses. These findings are contextualized by Santos, Pinto, Morais and Claudino-Sales20 , who indicate that the Northeast has the worst rates in terms of water supply interruptions and distribution losses, factors that are determinants for water quality. While the South and Central-West present the best results in these indices, explaining the results found.
Poor infrastructure conditions and inefficient services are linked to inequalities in the distribution of financial resources. The Northeast and North regions are the most affected in this respect, and this, according to Rodrigues et al.10, directly impacts public health. Reflecting this scenario, the Northeast is the region with the highest expenditure on diseases related to basic sanitation, reaching approximately R$96 million in costs associated with sanitary diseases in 2017. Bittelbrunn, Brinckmann, Andrett and Pfitscher9 point out that in the years 2012 and 2014, the Northeast region had the lowest level of efficiency in public spending on sanitation
The results for the Northeast region regarding water quality indicators, HDI, and efficiency level can be attributed to several factors, such as the region’s lack of transparency in information regarding basic sanitation20, the lack of coverage of public sanitation policies in rural areas19, and the fact that the region has the lowest rate of municipalities with some type of regulation, with the highest rates concentrated in the South and Southeast regions21. In 2012 and 2014, the Southeast region obtained the best efficiency performance in relation to expenditures in the sanitation sector, with resource management being the determining factor for these results9.
This result differs from those found in this study. Even though the Southeast region has the highest expenditure in the sector, it ranks second in both water quality indicators, suggesting a low level of efficiency, especially considering that it is the only region to exceed R$3 billion in expenses for basic sanitation services. Meanwhile, the South region spends approximately R$2,300,000,000 annually and has similar, even better, results in some indicators.
It is important to highlight that both investments and the provision of services in the sanitation sector are mostly public12. Santos, Kuwajima and Santana21explain that the sector’s deficits are not only consequences of a scarcity of resources, but also of the discontinuity of initiatives and the lack of prioritization of the sector as an instrument that promotes health and quality of life. Zuki et al.11 emphasizes that the lack of investment hinders both the fight against poverty and economic development, due to its social and environmental consequences.
Basic sanitation is essential for promoting public health and should be a priority for the State in its pursuit of minimizing its negative impacts on human development, considering that one of the Sustainable Development Goals (SDGs) created by the United Nations (UN) aims to guarantee the availability of water and sanitation for all, as well as the sustainable and integrated management of these services22. To achieve universal access to sanitation services by 2033, efficient management is necessary, as well as a better distribution of resources, as highlighted by Santos, Kuwajima and Santana 21 , it is essential that resources are directed towards capacity building, target agreements and commitment to efficient management.
Despite the contributions of this study, it is important to highlight that it is limited in its methodological procedures. The analysis performed by Brazilian macro-region generalizes the research, failing to consider the different particularities of each federative unit (UF) and municipality. Therefore, inferences at individual levels are erroneous and smooth out possible state and municipal variations22. The use of data from 2021 may not represent the reality of subsequent years, and the use of more specific indicators does not replace the Water Quality Index (WQI), which would provide a more complete index, thus giving more comprehensiveness to the study.
The integration of different databases was necessary, where the variables operating expenses for basic sanitation services and HDI were available by state, while water quality indicators were obtained from municipal analysis points. Integration based on the state may have resulted in a reduction in the level of detail of the water quality indicators. The results are limited to the variables studied; the research did not consider several particular aspects that characterize each region, such as the difference between urban and rural population, the rainy season which can affect water turbidity, among others.
Despite these limitations, the study provides a general overview of regional disparities, particularly regarding spending on basic sanitation services, showing that this remains a relevant and necessary topic. Future research could consider new variables, as well as the specific state and municipal characteristics of each region and how they might influence the results, to deepen the analysis and make the study more comprehensive.
CONCLUSION
This study highlighted the correlation between spending on basic sanitation, water quality, and human development in Brazil, demonstrating that the poor distribution of resources impacts not only infrastructure and service efficiency. The precariousness of sanitation services directly affects population development and public health, overburdening the public sector with avoidable expenses and widening regional inequalities resulting from resource scarcity, prioritization of urban centers, and sectoral inefficiency. This underscores the urgent need to prioritize basic sanitation as a key instrument for promoting public health and human development, in addition to the necessity of greater and more equitable investments.
