Volume 45 Number 179,
October - December 2014
Structural Determinants in The Spread
of Waterborne Diseases in Brazil
Marcos José de Souza, Elaine Fernandes and Lucas Vitor de Carvalho*
Date received: November 14, 2013. Date accepted: March 27, 2014

The main objective of this article is to analyze variables describing quality of living and the environment in the Brazilian population, focusing on waterborne diseases and sanitation. To do so, this work used factor analysis and a panel data econometric model. The results reveal that percentage increases in sanitation levels decrease the number of hospitalizations at an even greater percentage magnitude. This suggests that investing in sanitation would greatly impact the prevention of outbreaks, because factors such as coverage for water utilities in urban areas and water quality are some of the principal aspects that determine morbidity rates.

Keywords: Brazil, water, sanitation, environment, morbidity rates.


Bottlenecks in the national infrastructure are present in all economic sectors: the logistics industry, in its transportation routes and trade, education, in terms of insufficient physical infrastructure and investment to train people, and civil defense, looking at natural disasters and tragedies, such as sudden and recurring floods, among other examples. Endemic waterborne diseases emerge nearly every year in Brazilian cities due to multiple factors, including floods and contaminated drinking water. Floods seriously damage water supply networks and sewage systems, promoting the spread of diseases, and result in more people, especially children under the age of five, resorting to the Sistema Único de Saúde (Single Health System).

In Latin America and the Caribbean, children experience on average 3.3 episodes of diarrhea a year, but this number is as high as seven in some regions. In these areas of greater frequency, children may spend up to 15% of their lives with diarrhea (Ludwig et al., 1999). This leads to a seasonal spike in public health care costs, which require a tremendous capital injection from the central government. These costs could be reduced, and in some cases done away with altogether, if the causes behind these infections were taken seriously and investments were made in quality of life. With the resources currently allocated, it will not be possible to achieve a healthier population with lower public health costs and more efficient health care at clinics throughout the country. This work was written with this in mind, aiming mainly to analyze variables representing quality of life and environmental quality among the Brazilian population, focusing on waterborne diseases and deficient sanitation.

Areas at risk for landslides and other problems related to the water supply network, sewage system and trash collection are common in Brazil. Uncontrolled growth, such as the growth led by local political officials, has been weak in the face of ensuring efficient services in the long-term. In addition, the lack of serious plans focused on urbanization and improving the quality of living for the population, especially in light of the demographic and economic growth of Brazil, has led to structural problems in civil defense, exposing people to flooding, landslides and unpaved roads.

In terms of sanitation, high levels of chlorine residuals outside of the standards are frequently found in the water and the majority of Brazilian cities do not properly collect and treat wastewater. A work by Barcellos (2005) revealed that water pollution is not only a grave health issue, but is also the result of social and environmental problems. According to Ceddia et al. (2013), the appearance of infectious diseases is closely tied to economic growth and consequent environmental degradation. As such, universal access to supply chains and the growing risk of contamination from both land and underground sources can expose the population to chemical and biological agents. The poor quality of water collection and purification systems may even turn the water network itself into a potential source of spreading diseases.

Looking at the empirical literature, Ludwig et al. (1999), Guedes et al. (2000), Laurenti (2004), Barcellos (2005), Andrade and Silva (2005), Barreto et al. (2007), Confalonieri and Marinho (2007), Zucarelli et al. (2010), Ribeiro and Rooke (2010), Freitas and Ximenes (2012), Ayach et al. (2012) and others have sought to evaluate the relationship between health care spending and health quality. These studies suggest that the more the government invests in health infrastructure, the less it will have to spend on health care for the population. Data published by the Health Ministry corroborates these conclusions – for every R$1.00 invested in sanitation, R$4.00 is saved in medical costs (Funasa, Fundação Nacional de Saúde, 2011).

As indicated, there are many reasons behind endemic diseases, but few studies have sought to associate different types of data to analyze the behavior of how these diseases spread. By looking at various causes behind the same problem, the idea is to include not only results that indicate which variables are most correlated, but also to determine how public investment should be allocated to maximize its benefits in each area of action to combat endemic diseases, whether that is sanitation and civil defense, or other areas.

With that said, the overall objective of this work is to analyze the relationship between sanitation, natural disaster events and the number of hospitalizations for children under the age of five in Brazilian municipalities from 2005 to 2010. Concretely, this work will: a) identify the effect of sanitation and natural disasters on the number of admissions to the SUS (Single Health System), b) verify which municipalities face the most severe sanitation issues and c) determine which municipalities have the best sanitation and could be used as a reference for the rest.

This article is divided into three sections, in addition to the results, discussion and final considerations.


According to Sen (2008), the development strategies that countries choose should include efforts to create an economic, social, political and cultural environment that is favorable to its inhabitants. This is because the performance of each person will depend on the economic opportunities, political freedoms, social and educational options, stimuli and initiatives available to them. As such, the quality of life of each individual is closely tied to the real opportunities available due to collective achievements, both in the past and present.

Sachs (2007) adds to Sen (2008), saying that sustainable development must address, in addition to social and economic issues, environmental concerns. This means, for example, that improving quality of life and social equality must be central objectives (the ultimate goal) of the development model enacted. Similarly, economic efficiency and growth are essential ingredients, because it is unlikely that quality of life can be equitably improved if the economy is unable to grow. Environmental conservation and good judgment are also extremely important. In the absence of appropriate environmental conditions, countries will not be able to guarantee living standards and social equality for future generations.

Gallopín (1982) has also contributed to the discussion, emphasizing that environmental conditions are closely related to quality of life, because it is defined as the outcome of a person's health and sense of satisfaction.

Aiming to relate the variables representing quality of life and environmental quality, as mentioned earlier, Bojo et al. (2001) created the diagram shown in Figure 1, which shows the effects of environmental quality (third line down) on quality of life (rectangles) and the effect of both on overall welfare.

Humans are part of the environment with everything they produce. Humans both impact and are impacted by the environment. Quality of life and environmental quality are directly related, because air, water and soil pollution, as well as the extinction of ecosystems, directly affect individual welfare. Environmental quality could therefore be defined as the state in which the various dimensions of the environment exist. If the environment, which encompasses humanity with all of its practices and customs, is damaged in some way, the quality of life is reduced, which in turn affects environmental quality because the latter is a component of the former (Sachs, 2007).


Figure 1. Diagram of the Dimensions of Environmental Quality and Quality of Life

Source: Bojo et al. (2001).


Quality of life is the result of the exposure of an individual to adverse circumstances and is directly related to factors such as a healthy and productive environment, the presence of aesthetic and recreational spaces and participation in decision-making. It is also related to having opportunities to satisfy human desires and aspirations. Quality of life goes beyond the provision of basic needs (food, health, housing, clothing, education, employment and participation) and is a function of cultural values (Leff, 2004). In this way, if environmental quality is a component of quality of life and quality of life is, likewise, probably the most important component of development, it could be said that development depends on good environmental management (Sachs, 2007). In that sense, the impact of human beings on the environment and the environment on quality of life is ever more complex, both quantitatively and qualitatively (Leite, 2000).

In addition, quality of life depends on environmental quality to achieve balanced and sustainable development.1 This idea of quality of life aims to break the homogeneous parameters of well-being and create opportunities for new indices to articulate the costs of growth with cultural values and the potential of nature, as well as complement objective metrics with subjective perceptions. In this way, quality of life will allow us to reflect on social equality, as well as ecological and cultural diversity (Leff, 2004).

Briefly, the meaning of quality of life involves values that regulate social behavior, which are associated not only with income levels or wealth distribution, but also criteria related to reproductive health, maternity and paternity and environmental self-regulation mechanisms. If environmental quality is impaired, this will lead to a pathological state in which the diseases of poverty (like cholera and dengue fever),2 which come from contaminated air, soil, water resources and toxic pesticides, tend to emerge.

In light of worsening environmental problems, policy actions must aim to spread environmental awareness among the population through education. In addition, our understanding of environmental issues must be based on a social perspective of the environment with an emphasis on cultural criteria, which will in turn require specific public policies. The big challenge will be to pursue quality of life and environmental quality objectives in such a way that they mutually reinforce each other.3


Brazil is the fifth-largest country in the world in terms of territory size, with over eight and a half million square kilometers. The country is home to a broad range of geomorphological, environmental and weather conditions. These environmental differences are fundamental to understanding the ways in which social structures interact with how the land is occupied. These factors help determine the culture and economic activities conducted in each place, create a regional identity and forge habits unique to each reality. The way in which the Brazilian territory was occupied is indicative: cultural habits and customs arrived alongside colonization, further exacerbating regional differences. Because national development came about largely focused on local economic growth (sugarcane in the Northeast, gold in Minas Gerais and coffee to the west of São Paulo and in Vale do Paraíba), the concentration of resources is a historical structural issue.

All of these discrepancies have created unique environments in which endemic diseases proliferate for different reasons. As explained by Ayach et al. (2012: 60):

A society consisting of different environments requires us to look beyond the physical aspects of the environment and become fully familiar with the living conditions of the residents, the social classes, the features of the neighborhoods in which people live, as well as income, occupations, education, culture and, importantly, their expectations or visions of the world, which are linked to their environmental perceptions and values.

Aiming to obtain appropriate knowledge of the living conditions of the population, Confalonieri and Marinho (2007) created the Vulnerability Indices. They vary between 0 and 1 and assign values inversely proportional to how susceptible a society is to diseases and the appearance of diseases, whether they emerge due to social, climate-related or epidemiological reasons. Maps 1, 2 and 3 show the values of each indicator by Brazilian state.

The maps reveal that higher rates are found in the North and Northeast. This means that these states have a perverse combination of low socioeconomic indicators, high climate vulnerability and high epidemiological vulnerability.

Looking specifically at the Northeast, according to Guedes et al. (2000), the number of hepatitis A cases in Campina Grande-PB grew with the drought. As a result of water rationing, people began to consume water from alternative sources, such as rainwater, which is more likely to be contaminated due to weather and storage conditions. The drought in the North also led to the emergence of diseases such as cholera. As Confalonieri and Marinho (2007) demonstrated, the lack of rainwater left the local population without sufficient water supply, reduced water volume in rivers and created puddles on the shore, increasing the concentration of Vibrio cholerae. It is worthwhile to remember that both of these situations happened in areas that were socioeconomically disadvantaged, where both the water supply and sanitation are highly deficient.

Although the North and Northeast had the highest vulnerability indices, states such as Minas Gerais and São Paulo also deal with socioeconomic, climate-related and epidemiological problems Deficient infrastructure and education contribute to the difficult situation and further intensify issues facing the region (Ayach et al., 2012).


Map 1. Epidemiological Vulnerability Index

Source: Confalonieri and Marinho (2007).



Map 2. Socioeconomic Vulnerability Index

Source: Confalonieri and Marinho (2007).



Map 3. Climate-Related Vulnerability

Source: Confalonieri and Marinho (2007).


As such, this work centers on analyzing the states of Minas Gerais and São Paulo because they have a large amount of data available on sanitation and their vulnerability indices were close. Moreover, the natural disasters dimension only covers floods and landslides, which are recurring problems in the states selected.

This complex panorama encompasses diverse factors that not only influence the final results, but also influence each other, reinforcing the argument in favor of an integrated information system and monitoring of morbidity and mortality resulting from extreme climate events. This will require cooperation between the Health, Civil Defense and Public Security Departments.


3.1. Econometric Model

Panel data techniques were used in this study to explore the consequences of varying conditions of sanitation and severe climate events over time, in relation to morbidity (hospitalization) indices among children aged one to four years old at Single Health System (SUS) facilities. We obtained a six-year data series with a sample of 69 cities in the states of Minas Gerais and São Paulo. Both offered lots of data on sanitation and were relatively similar in terms of the vulnerability indices described earlier. In light of the expansion of supply networks in these states and their socioeconomic and climate-related vulnerability, the choice was made not to include the drought variable as a natural disaster indicator in this model.

Because there are many cities with structural diversity, this method allows us to maintain constant certain specific peculiarities of each of them. Expression (1) summarizes the model used.


Where m is the number of admissions of children under the age of five, s is the sanitation indicator and d is the indicator of natural disaster events. The sanitation indicator was determined using principal component factor analysis4 of the following variables (some in scalar values and others in percentages): water connections to the network by active economies, extension of water network connection by link, index of urban water service, index of treated wastewater with respect to water consumed, index for total water provision, frequency of chlorine residual analysis within standard ranges, frequency of turbidity analysis within standard ranges, frequency of total coliform analysis within standard ranges, index for productivity of total personnel-equivalent.

To choose the model, it was necessary to conduct the Chow, Hausman and Breusch-Pagan LM tests. It was not necessary to detect the presence of self-correlation and heterocedasticity using the Wooldridge and White tests, because the random effects model was more than adequate. For further details, see Gujarati and Porter (2011).

3.2. Data Source and Processing

3.2.1. Health Data

The principal diseases specified in the current literature are part of the ICD-10 (international classification of diseases). They belong to two groups, infectious and parasitic diseases and digestive system diseases. The idea was to reduce the effects of under-reporting by using the morbidity data of people aged one to four. All hospitalization records were acquired from the SUS database, known as DATASUS.

3.2.2. The National Sanitation Information System (SNIS)

Provided by the Ministry of Cities, this system is a voluntary sample of data on the management, costs, functionality and quality of water services, sanitation, and collection and disposal of solid waste. A sample of 69 municipalities was taken from the SNIS for a period from 2005 to 2010, with data on the density of active saving and the extension of the water grid by connection (a grid whose water connections are more exclusive to a single region or residence reduces the chance of contamination because only a single water connection would be contaminated), the scope of water services in the urban environment and total for municipalities, the percentage index of water treatment with respect to water consumed (how much of the drinking water was treated after usage by the service provider) and the frequency of test results within standard ranges for chlorine residual levels, turbidity and total coliform bacteria in water.

3.2.3. The Brazilian Atlas of Natural Disasters

Developed by the Universidade Federal de Santa Catarina in 2010 and 2011, this is a collection of information from the State Civil Defense Coordination Department that offers a geographical characterization of local zones and presents data from 1991 to 2010 for sudden floods, gradual floods, wind storms and/or cyclones, hale, land movements (landslides), forest fires, linear and river erosion, falling water levels and droughts. The main phenomena analyzed that exacerbate the spread of diseases were sudden flood events (increase in rain precipitation followed by flooding, with violent and rapid water level rises), gradual floods (which occur in lower altitude places and as a result of landslides, and are part of the group of natural disasters related to geomorphology) and land movements in the cities selected.

3.2.4. Variables

The sanitation variable used all of the data obtained from the SNIS to produce a single indicator that would provide a vision of overall sanitation every year in each city. This data offered information on water supply coverage, service quality, the quality of water purification services and the volume of wastewater treated with respect to water consumed. This indicator provided a broader perspective of the levels of sanitation most decisive to preventing endemic contamination.

The climate-related variable analyzed the total impact of landslides and flooding (both sudden and gradual) that produced "natural disasters." Zucarelli et al. (2010) analyzed this same data. Because these events are destructive, they damage dams and sewage systems, contaminating water veins, which often supply urban areas.

The diseases analyzed mainly belong to the infectious and parasitic disease group, while others were treated as gastrointestinal illnesses. All of these spread through contact and ingesting contaminated water (the life cycle of the infectious agent present in water channels or puddles) and include: cholera, typhoid and paratyphoid fever, shigellosis, amoebiasis, diarrhea and gastroenteritis with a presumed infectious origin, icterohemorrhagic leptospirosis, non-specified leptospirosis, yellow fever, dengue fever, hemorrhagic fever due to the dengue virus, malaria derived from plasmodium falciparum, plasmodium vivax and plasmodium malariae, other forms of malaria observed in parasitological analyses, cutaneous, mucocutaneous or visceral leishmaniasis, non-specified leishmaniasis, schistosomiasis, dranculiasis, heartworm, hookworm, other infectious intestinal diseases, other forms of leptospirosis, other forms of viral hepatitis, non-specified malaria, other helminths and trematode infections. All data describes total number of admissions every year in each city, as provided by the SUS database, DATASUS.

Seasonal outbreaks of vector-transmitted diseases are influenced by temperature increases and precipitation. The number of hospital admissions for some diseases could be prevented by vaccinating the population. Because the data discussed here is annual, the model does not address seasonal variation of hospital admissions, nor does it consider vaccinations.


This study used panel data to examine the effects of varying sanitation conditions and severe climate events over time, as related to morbidity (hospitalization) indices for children under the age of five. This approach was appropriate because it allowed certain peculiarities of the municipalities with diverse structural differences to remain constant.

First, tests were conducted to choose the model. Table 1 displays the values obtained.

This table reveals that the Chow and Breusch-Pagan LM tests were statistically significant, unlike the Hausman test. This indicates that the model that best fit the sample was the random effects model. It was not necessary to conduct Wooldridge and White tests because there was no self-correlation or heteroscedasticity in the random effects model. For that reason, the model is statistically significant. The results of the estimated model are shown in Table 2. In terms of the F-test, the hypothesis that all coefficients were statistically equal to zero is rejected.

The sanitation variable was statistically significant with 1% of probability. The value of the coefficient shows that in percentage terms, a 1% increase in health quality reduces the number of beds occupied in the SUS by 1.02%. This outcome demonstrates the importance of increasing basic sanitation to improve the quality of life for the population, because preventive health care produces a lower death rate, saving public resources.

It should also be mentioned that the worst sanitation levels were found in the Minas Gerais municipalities. The city of Barbacena stood out in this ranking. With a supply system that is poorly distributed and barely complete, less than 12% of the wastewater in Barbacena is treated and only 72% of water samples had total coliform, chlorine and turbidity levels within standard ranges. Together, these factors confirm the generally poor health seen in the city (SNIS, 2012).

The municipality of São Paulo that ranked last in sanitation was Santa Isabel, which, despite having good quality of water, does not treat its collected wastewater in any way. Wastewater is thrown into rivers and streams in the city and contaminants accumulate in the Jaguari dam, which, besides being important for energy supply and generation, is also key to sustaining local fishermen. However, there were very few hospitalizations during the period analyzed in this city. This would suggest that the municipality is not directly affected by its excrement, meaning that the lack of sanitation services does not have direct consequences for the city itself, but does come at a social cost for neighboring residents that must deal with contaminated rivers due to the lack of wastewater treatment in Santa Isabel. The same is true of the city of Guanhães in Mina Gerais, where morbidity levels are higher than in Santa Isabel, but still low taking into account that they do not treat wastewater.

On the other end of the spectrum, the best results appeared in the municipalities of Jundiaí and Vinhedo in the state of São Paulo and Uberlândia in the state of Minas Gerais. These cities have well-distributed and comprehensive water supply systems within their urban boundaries, and their drinking water is also high quality. These municipalities could serve as an example to follow for the others under study.

Despite the lack of wastewater treatment, the city of Lambari-MG had excellent water quality during the first three years of the sample, pushing the city to a high spot on the ranking, with only seven hospitalizations during the time period. In subsequent years, however, likely due to the disorderly growth of the city, the total water supply index no longer covered nearly the entire population and fell to serving only 75% of neighborhoods. Besides reducing the overall sanitation index, there were consequences for the health system. The low number of hospitalizations (seven) during the first three years rose to 24 in the last three, likely due to deficient water supply in the urban zone. This failure meant the population had to drink more water from alternative sources, increasing the number of hospitalizations by 343%.

Although the natural disasters value had a low value for the coefficient found, it was statistically significant in the 10% significance model. This demonstrates that accidents due to extreme climate events, such as storms and floods, are part of the daily lives of people in Brazil and significantly affect their quality of life. Given their geography, Minas Gerais and São Paulo had a considerable number of houses located in at-risk zones, such as slopes and lowlands. This means that these states face serious flooding and landslide problems that produce enormous losses, because on top of contaminating important water veins (sometimes causing fatal diseases), they can directly cause the death of an innumerable number of people who drown or become buried in the disasters.

In the state of São Paulo, the number of cities with recorded damage and the number of displaced people due to strong rains are both on the rise. Comparing 2012 and 2013 (up until now), for example, these figures rose from 58 and 1,638 (2012) to 146 and 4,499 (2013), respectively (Folha de São Paulo, 2013).

Keeping in mind these and other events, it is time to develop integrated systems with data on morbidity and mortality derived from these climate events, including information from health, civil defense and public security departments. This system would help identify the victims of landslides and floods, as well as their immediate causes and consequences, which would make authorities more agile and efficient in their response to the problem.


This study aimed to examine the effects of sanitation and natural disaster events on the number of children under the age of five hospitalized in Brazilian municipalities from 2005 to 2010. In light of the fact that spending could be reduced with better health practices and water resource management, it becomes clear why we must understand the causes underlying this problem. The issue is even more pressing if we consider climate change and the weather phenomena that result and will frequently increase contamination by creating environments more friendly to disease agents.

This study reveals that every percentage increase in sanitation produces a percentage decrease of the same magnitude in the number of hospitalizations. This elasticity suggests that investing in health will help prevent outbreaks, because factors such as coverage of water services in the urban environment and quality of treated water were some of the principal determinants of morbidity levels. In this way, better sanitation can reduce the number of deceased. For this to occur, there must be sufficient infrastructure and available capital. The state of São Paulo, for example, had better sanitation conditions during the time period analyzed because its cities have better infrastructure and capital availability. Meanwhile, the state of Minas Gerais was in a rather more precarious situation because its cities face worse sanitation and conditions for contagion. Moreover, the infrastructure and capital available in the state of Minas Gerais is less than that available in São Paulo.

Mindful of the above, it was observed that morbidity could be mitigated, thereby reducing costs and improving the quality of life for society. This would be possible through the joint efforts of different spheres of the government (health, civil defense and public security departments) starting with the development of information systems on morbidity and mortality. These systems would help recognize victims and determine the principle causes and consequences of events, which would improve health care for victims and prevent future problems of contamination through water veins.

Finally, the positive results in the realm of sanitation should be reinforced by activities that entail a profound regional analysis, aiming to create regional environmental and health action plans. The need for an integrated action center and monitoring of sanitation, climate and civil defense has proved significant. These aspects are extremely valuable to improving living conditions and the efficiency of public health services.


Andrade, Rodrigo da Rocha, and Julce Clara Silva (2005), “Saneamento e saú¬de ambiental: hospitalizações por doenças infecciosas intestinais no RS”, Anais do 23º Congresso Brasileiro de Engenharia Sanitária e Ambiental, Campo Grande-MT, pp. 1-9.

Ayach, Lucy Ribeiro; Solange Therezinha de Lima Guimarães; Nanci Cappi, and Carlos Ayach (2012), “Saúde, saneamento e percepção de riscos ambien¬tais urbanos”, Caderno de Geografia, Brazil, vol. 22, no. 37, pp. 47-64.

Barbier, Edward B. (2000), “The economic linkages between rural poverty and land degradation: Some evidence from Africa”, Agriculture, Ecosystems and Environment, vol. 82, no. 1, pp. 355-370. Barcellos, Cristovam (coord.) (2005), Desenvolvimento de indicadores para um sistema de gerenciamento de informações sobre saneamento, água e agravos à saúde relacionados, Brasil, Fiocruz, pp. 48.

Barreto, Maurício L. et al. (2007), “Effect of city-wide sanitation programme on reduction in rate of childhood diarrhea in northeast Brazil: assessment by two cohort studies”, The Lancet, vol. 370, no. 9 599, pp. 1 622-1 628.

Bojö, J. et al. (2001), Environment chapter, poverty reduction strategy papers’ source book, Washington, World Bank, p. 214.

Brundtland, Gro Harlem (1987), “Our common future”, UNEP, Nairobi, Kenya (consulted December 12, 2012), available at: .

Ceddia, M. G. et al. (2013), “A complex system perspective on the emergence and spread of infectious diseases: Integrating economic and ecological as¬pects”, Ecological Economics, Amsterdam, vol. 90, pp. 124-131.

Confalonieri, Ulisses E. C., and Diana P. Marinho (2007), “Mudança climática global e saúde: perspectivas para o Brasil”, Revista Multiciência, Brazil, vol. 8, pp. 48-64.

Ekbom, Anderes, and Juan Bojö (1999), “Poverty and environment: Evidence of links and integration into the country assistance strategy process” (consul¬ted October 20, 2011), available at: .

Folha de São Paulo (2013), Chuvas já provocaram 25 mortes em São Paulo desde dezembro (consulted September 23, 2013), available at: .

De Freitas, Calos Machado, and Elisa Francioli Ximenes (2012), “Enchentes e saúde pública - uma questão na literatura científica recente das causas, con¬sequências e respostas para prevenção e mitigação”, Ciênc. Saúde Coletiva, Brazil, vol. 17, no. 6, pp. 1 601-1 616.

Funasa-Fundação Nacional da Saúde (2011), “Funasa leva água tratada a comunidades rurais em Jaraguari (MS)” (consulted October 30. 2013), available at: .

Gallopín, G. C. (1982), El ambiente urbano y la planificación ambiental, Mé-dio Ambiente y Urbanización, Buenos Aires, CLACSO/CIFCA, p. 186.

Guedes, Dionéia Garcia de Medeiros; Salomão de Andrade Pascoal, and Beatriz Suzana Ouruski de Ceballos (2000), “Doenças de veiculação hídrica: dia¬rreia e hepatite Campina Grande-PB”, Anais do XXVII Congresso Interame¬ricano de Engenharia Sanitária e Ambiental, Brazil, pp. 1-8.

Gujarati, Dadomar N., and Dawn C. Porter (2011), Econometria básica, Porto Alegre, AMGH, p. 924.

Laurenti, Ruy; Jorge M. H. P. Mello, and Sabrina Léa D. Gotlieb (2004), “A confiabilidade dos dados de mortalidade e morbidade por doenças crôni¬cas não-transmissíveis”, Ciência & Saúde Coletiva, Brasil, vol. 9, no. 4, pp. 909-920.

Leff, Henrique (2004), Saber ambiental, Petrópolis, 3rd Edition, Ed. Vozes, pp. 87.

Ludwig, Karin Maria et al. (1999), “Correlação entre condições de saneamen¬to básico e parasitoses intestinais na população de Assis, Estado de São Paulo”, Rev. Soc. Bras. Med. Trop., Brasil, vol. 32, no. 5, pp. 547-555.

Parikh, J. (2002), “Poverty-environment-development nexus international”, Journal of Global Environmental, vol. 2, pp. 344-365.

Ribeiro, Júlia Werneck, and Juliana Maria Scoralick Rooke (2010), Saneamento básico e sua relação com meio ambiente e saúde pública, Juiz de Fora-MG, UFJF, p. 28.

Sachs, Ignacy (2007), Rumo à ecossocioeconomia: teoria e prática do desenvolvi-mento, Ed. Cortez, p. 472.

Sen, Amartya Kumar (2008), Desenvolvimento como liberdade, São Paulo, Schwarcz, p. 409.

Da Veiga, José Eli (2008), “Indicadores socioambientais: evolução e perspec¬tivas”, Revista de Economia Política, Brazil, vol. 29, no. 4, pp. 421-435.

Zucarelli, Marcos Cristiano et al. (2010), “Monitoramento das ações em sa-neamento básico e vulnerabilidade em áreas de risco em Minas Gerais”, Conferência Internacional da Rede, Brasil, vol. 1, pp.1-13.

*Universidade Federal de Viçosa, Brazil. mjs05carvalho@gmail.com, eafernandes@ufv.br, lucasvitor.cs@gmail.com, respectively

1 With all of these events, the World Commission on Environment and Development (WCED) worked between 1984 and 1987 on creating an agenda for global change. The result was the report Our Common Future (Brundtland Report), which examined critical development and environmental problems. It conceptualized the term sustainable development, which should satisfy the needs of the present in an equitable manner without compromising the survival and prosperity of future generations (Brundtland, 1987).

2 Studies such as Ekbom and Bojo (1999), Barbier (2000) and Parikh (2002) have demonstrated the relationship between poverty and the environment.

3 Both the national and international literature has some examples of building indices that incorporate environmental conditions. For further details, see Veiga (2008).

4 The factor analysis chosen aims to investigate the dependence of all of the variables to observe the degree of association, a formula of estimated values, and establish statistical significance levels for the variables.

Published in Mexico, 2012-2017 © D.R. Universidad Nacional Autónoma de México (UNAM).
PROBLEMAS DEL DESARROLLO. REVISTA LATINOAMERICANA DE ECONOMÍA, Volume 48, Number 191, October-December 2017 is a quarterly publication by the Universidad Nacional Autónoma de México, Ciudad Universitaria, Coyoacán, CP 04510, México, D.F. by Instituto de Investigaciones Económicas, Circuito Mario de la Cueva, Ciudad Universitaria, Coyoacán,
CP 04510, México, D.F. Tel (52 55) 56 23 01 05 and (52 55) 56 24 23 39, fax (52 55) 56 23 00 97, www.probdes.iiec.unam.mx, revprode@unam.mx. Journal Editor: Alicia Girón González. Reservation of rights to exclusive use of the title: 04-2012-070613560300-203, ISSN: pending. Person responsible for the latest update of this issue: Minerva García, Circuito Maestro Mario de la Cueva s/n, Ciudad Universitaria, Coyoacán, CP 04510, México D.F., latest update: Nov 13th, 2017.
The opinions expressed by authors do not necessarily reflect those of the editor of the publication.
Permission to reproduce all or part of the published texts is granted provided the source is cited in full including the web address.
Credits | Contact

The online journal Problemas del Desarrollo. Revista Latinoamericana de Economía corresponds to the printed edition of the same title with ISSN 0301-7036