This study analyzed a 22-year historical series of outbreaks of waterborne and foodborne diseases, aiming to provide a mapping of the main associated pathogens, regions with the highest incidence, foods involved, and places of infection. The results, in the scenario of Brazilian macro-regions, showed heterogeneity in the macro-regional microbiological profile, with the Southeast region being the one with the highest number of diseases caused by viruses, while in the others there was bacterial predominance. Of the 78 organisms reported, 10 caused more than 95% of illnesses. Among them, Escherichia coli was the bacteria that grew the most in the number of notifications from 2011 to 2021. Water was one of the main vehicles of transmission, in addition to foods that showed classic behavior in terms of microbiological transmission. Weaknesses were observed in the records that limit the carrying out of more specific analyses due to the large number of inconsistent, ignored, or inconclusive cases, which, in some parameters, exceeded 50% of notifications. This research highlights the need to strengthen the health system, so that there is a more specific and effective diagnosis of factors related to the spread of infectious diseases.

  • E. coli was the microorganism with the highest incidence from 2011 to 2021 in Brazil.

  • Ten pathogens cause 95% of waterborne and foodborne diseases (2000–2021).

  • More than 50% of Waterborne and Foodborne Diseases (WFD) records do not have identified etiological agents.

  • Water is a fundamental vehicle for transmitting diseases directly or indirectly.

  • With the exception of the Southeast region, bacterial infections prevailed in the other regions.

Epidemiology is a fundamental science for expanding the capacity of health systems to understand the dynamics of diseases. According to Sims & Kasprzyk-Hordern (2020) and Van Doorn (2021), especially since the 20th century, epidemiological analyses have served as powerful tools for the state in the search for solutions involving public health. Over the years, there has been a growing need to expand research and to utilize analytical tools to generate qualitative and quantitative indicators with greater precision to define relationships between pathogens and hosts (Brazil 2024a).

In the context of infections, the environment brings together a range of factors that can be associated with more than 80% of the main diseases and injuries in the world and are among the leading causes of death (WHO 2017). Considering these factors, epidemiology seeks to highlight the variety of factors related to the diffusion and spread of diseases, as well as their frequency, mode of distribution, evolution, and prevention methods (Straif-Bourgeois et al. 2023). In this way, it is possible to map possible locations and populations that are more vulnerable to illness, as well as identify the most recurrent organisms and trace their dissemination routes.

Considering the need for a holistic view of social, behavioral, economic, cultural, and environmental factors, epidemiological bulletins from the Brazilian Ministry of Health have emerged as important resources for guiding the actions of professionals working in different areas related to health (Brasil 2024b). Despite this, statistical studies are still scarce in terms of breadth and depth of results and discussions related to reported cases. In this context, some of the factors that can make epidemiological reports more effective include identifying regions with a higher occurrence of certain diseases, reducing people's exposure to contaminated environments and consumables, assessing the infectious potential of each etiological agent, and identifying the increase in infections caused by pathogens over time (FIOCRUZ 2022).

In view of this, the following new initiatives for analyzing and reporting global data, which resemble these factors, stand out: the Global Antimicrobial Resistance Surveillance System (GLASS AMR), a project that maps microbial resistance in different countries around the world, and the National Notifiable Diseases Surveillance System (NORS), which provides data on notifications of outbreaks of diseases related to waterborne, foodborne, and environmental transmission in the USA (CDC 2022).

Although researchers have made efforts to develop mappings and provide consistent data on the scenario of these diseases, there are still difficulties in many countries, especially the least developed ones, in providing accurate and consistent information that allows for the effective preparation of epidemiological reports. This may be due to the lack of adequate infrastructure, limited resources, and lack of training in epidemiology. In Brazil, although there is no high coverage of epidemiological information, there are databases that allow the analysis of some factors related to this area. An example of this is the report on waterborne and foodborne diseases (WFD) published by the Ministry of Health (Brazil 2022).

Considering the above, it is important to highlight that this is one of the first epidemiological studies with a statistical approach on WFD in the five macro-regions of Brazil, providing support for the advancement of new research in the area. In the country, the discussion on the heterogeneity of environmental, social, political, and economic factors that characterize the territory must be considered in the scenario of WFD. This requires an interdisciplinary vision that encompasses human, animal, and environmental health. This approach provides information on the assertive basis of actions and public policies aimed at the prevention and treatment of these health problems. Thus, this study aimed to analyze a historical series of 22 years of epidemiological data on WFD in Brazil and map the incidence of the main associated pathogens, the regions of greatest occurrence, the foods involved, and the places of infection.

This research, concentrated on secondary source data, covers notifications of WFD outbreaks in the five Brazilian macro-regions: North, Northeast, Midwest, Southeast, and South between the years 2000 and 2021. The incidence analysis was carried out in two periods as a means of comparison (2000–2010 and 2011–2021), considering the availability of population data from the 2010 and 2022 census.

The main discussion focused on a small group of etiological agents (n = 10) that accounted for the largest number of reported patients. The 10 etiological agents represented 95.24% of patient notifications; therefore, efforts were concentrated on understanding the dynamics of this group, namely, Bacillus cereus, Clostridium, Coliforms, Escherichia coli, norovirus, rotavirus, Salmonella, Shigella, Staphylococcus, and hepatitis virus in the two periods proposed by this work.

Given the large number of variables, we decided to analyze two factors: (1) contaminated food and (2) places of contamination. We focused on the four main etiological agents, which belong to the previous group (n = 10) and cause WFD, namely, Staphylococcus, E. coli, Salmonella, and rotavirus. Of the total number of sick people, these microorganisms represented more than 70% of all notifications.

Obtaining, characterizing, and defining the data to be analyzed

The historical data (https://docs.google.com/spreadsheets/d/1XT4iffaWUcMgU_t-Q0mKZhX1UHUJsJxi/edit#gid=1026946243) were obtained through open access to the Ministry of Health page, presented in a single Microsoft Excel® document containing two tables. The first table contains a time series from 2000 to 2006, while the second covers the period from 2007 to 2021.

The data used for this study were region of notification, year of notification, etiological agent, food causing the outbreak, place of occurrence of the outbreak, and the total number of patients. The study evaluated the etiological agents as the response variable and examined their possible relationships with other parameters.

Data analysis

Numerical and descriptive data were processed using Microsoft Excel® software and analyzed using Past 4.13® software. To verify possible differences in incidence from one region to another, the data were tested for the nature of the distribution using the Shapiro–Wilk test and subsequently subjected to normalization using the logarithmic scale (Li et al. 2017; Cook et al. 2020). The one-way ANOVA test was used, followed by the Tukey post-hoc test, using a 95% confidence interval. SigmaPlot 12.0® software and QGIS® software were used for graphic demonstrations and maps, respectively.

Incidence of diseases caused by etiological agents

The incidence was calculated by dividing the number of patients by the exposed population in each region and then multiplying the result by 100,000. To define the number of people exposed in each region, the population measured by the 2010 census for the period 2000–2010 and the population from the 2022 census for the period 2011–2021 were adopted.

In total, 78 etiological agents were recorded, including bacteria, viruses, worms, and protozoa. For unidentified cases, the terms ‘other, ignored, inconclusive and inconsistent’ were found. Among the notifications, the following nominations of microorganisms were identified (Table 1).

Table 1

Microorganisms identified in the WFD outbreak notification system between the period 2000 and 2021

MicroorganismSubspeciesMicroorganismSubspecies
Bacteria Aeromonas caviae Bacteria Pseudomonas aeruginosa 
hidrophila  putida 
veronii  spp. 
spp. Salmonella diarizonae 
B. cereus   enteritidis 
Campylobacter jejuni  D group 
spp.  hadar 
Cianobactérias   johannesburg 
Citrobacter freundii  newport 
spp.  paratyphi 
Coliforms total  typhimurium 
fecal  spp. 
Enterobacter aerogenes Shigella dysenteriae 
hormaechei  flexneri 
spp.  sonnei 
Enterococcus   spp. 
E. coli spp. Staphylococcus aureus 
Enteroaggregative Escherichia coli (EAEC)  spp. 
Enterohemorrhagic Escherichia coli (EHEC) Streptococcus perfringens 
Enteroinvasive Escherichia coli (EIEC)  spp. 
Enteropathogenic Escherichia coli (EPEC) Serratia adorifera  
Enterotoxigenic Escherichia coli (ETEC) Vibrio parahaemolyticus 
Klebsiella pneumoniae  cholerae 
spp.  cholerae non O1/non O139 
Listeria monocytogenes  cholerae O1 
spp.  metschnikovii 
Plesiomonas shigelloides   parahaemolyticus 
Proteus    
Protozoa Amebíase  Protozoa Entamoeba histolytica 
Clostridium botulinum spp. 
difficile Giardia spp. 
perfringens Tripanossoma cruzi  
sulfito-redutor Toxoplasma gondii  
spp. Urbanorum spp.  
Cryptosporidium spp.    
Viruses Adenovirus  Viruses Norovirus  
Astrovirus  Rotavirus  
Enterovirus  Hepatitis A virus  
Coxsackievirus    
Worms Ascaris lumbricoides     
Enterobius vermiculares     
Strongiloide     
MicroorganismSubspeciesMicroorganismSubspecies
Bacteria Aeromonas caviae Bacteria Pseudomonas aeruginosa 
hidrophila  putida 
veronii  spp. 
spp. Salmonella diarizonae 
B. cereus   enteritidis 
Campylobacter jejuni  D group 
spp.  hadar 
Cianobactérias   johannesburg 
Citrobacter freundii  newport 
spp.  paratyphi 
Coliforms total  typhimurium 
fecal  spp. 
Enterobacter aerogenes Shigella dysenteriae 
hormaechei  flexneri 
spp.  sonnei 
Enterococcus   spp. 
E. coli spp. Staphylococcus aureus 
Enteroaggregative Escherichia coli (EAEC)  spp. 
Enterohemorrhagic Escherichia coli (EHEC) Streptococcus perfringens 
Enteroinvasive Escherichia coli (EIEC)  spp. 
Enteropathogenic Escherichia coli (EPEC) Serratia adorifera  
Enterotoxigenic Escherichia coli (ETEC) Vibrio parahaemolyticus 
Klebsiella pneumoniae  cholerae 
spp.  cholerae non O1/non O139 
Listeria monocytogenes  cholerae O1 
spp.  metschnikovii 
Plesiomonas shigelloides   parahaemolyticus 
Proteus    
Protozoa Amebíase  Protozoa Entamoeba histolytica 
Clostridium botulinum spp. 
difficile Giardia spp. 
perfringens Tripanossoma cruzi  
sulfito-redutor Toxoplasma gondii  
spp. Urbanorum spp.  
Cryptosporidium spp.    
Viruses Adenovirus  Viruses Norovirus  
Astrovirus  Rotavirus  
Enterovirus  Hepatitis A virus  
Coxsackievirus    
Worms Ascaris lumbricoides     
Enterobius vermiculares     
Strongiloide     

Of the total number of WFD notifications recorded (375,258 patients), around 60% had their etiological agents identified and separated by class (Table 2).

Table 2

Class of etiological agents of WFDs (2000–2021) and percentage of patients with registered notifications

ClassReported patients (%)
Bacteria 70.56% 
Virus 27.24% 
Worms 0.01% 
Chemicals and toxins 0.27% 
Protozoa 1.87% 
Cyanobacteria 0.05% 
ClassReported patients (%)
Bacteria 70.56% 
Virus 27.24% 
Worms 0.01% 
Chemicals and toxins 0.27% 
Protozoa 1.87% 
Cyanobacteria 0.05% 

In this sense, a predominance of the bacterial class (seven agents) over the viral class (three agents) was observed both in the first and second periods of analysis. It was found that, despite some regions providing high exposure, there was a low incidence of patients due to the population size (Figure 1).
Figure 1

Incidence of infections, presented on a logarithmic scale, is categorized by the region of the country for the years (a) 2000–2010 and (b) 2011–2021, considering the etiological agents that represented more than 95% of all reported cases of WFDs in the Ministry of Health's report WFD. *The minimum incidence values represented on the map are the lowest non-zero values identified in the reports. Therefore, null values are not considered during the preparation of maps, since proportional point symbol maps do not accept null values. **A statistical analysis was carried out using the Tukey test, comparing the increase or reduction in the incidence of etiological agents in the two periods studied (2000–2010 and 2011–2021). When the etiological agent was represented with the same lowercase letter in both periods, no statistically significant differences were observed (p > 0.05).

Figure 1

Incidence of infections, presented on a logarithmic scale, is categorized by the region of the country for the years (a) 2000–2010 and (b) 2011–2021, considering the etiological agents that represented more than 95% of all reported cases of WFDs in the Ministry of Health's report WFD. *The minimum incidence values represented on the map are the lowest non-zero values identified in the reports. Therefore, null values are not considered during the preparation of maps, since proportional point symbol maps do not accept null values. **A statistical analysis was carried out using the Tukey test, comparing the increase or reduction in the incidence of etiological agents in the two periods studied (2000–2010 and 2011–2021). When the etiological agent was represented with the same lowercase letter in both periods, no statistically significant differences were observed (p > 0.05).

Close modal

In general, the incidence of all etiological agents decreases from the first (2000–2010) to the second (2011–2021) period analyzed. For the period from 2000 to 2010, the highest incidences among the 10 etiological agents analyzed were Salmonella > rotavirus > Staphylococcus, respectively. On the other hand, for the second period from 2011 to 2021, the highest incidences were E. coli>Salmonella>Clostridium, respectively.

The three classes of viruses evaluated showed no statistical difference between them during the evaluation of the two periods studied (test and value; p > 0.05). Regarding the class of bacteria, analyzing the two study periods, a statistical difference (test and value; p < 0.05) of the etiological agents was observed: Salmonella, B. cereus, Staphylococcus, and Clostridium.

In the North region, the highest incidence was attributed to the etiological agent B. cereus (1.26), and there was no incident in the first period for the etiological agents Shigella and norovirus. For the second period analyzed (2011 to 2021), the highest incidence of the etiological agent in the North was E. coli (0.92), while Clostridium, Shigella, and norovirus reported no presenting cases (null incidence).

In the Northeast, the highest incidence was due to the etiological agent Salmonella (0.91), with no incidence for norovirus in this period (2000–2010). For the second period analyzed (2011–2021), the highest incidence of etiological agent was E. coli (1.05), with the lowest incidence being hepatitis A (0.13).

In the Midwest, the highest incidence was related to the etiological agent Salmonella (1.38), and for norovirus, the incidence was zero in the period from 2000 to 2010. In the second period analyzed, the highest incidence of the etiological agent was Salmonella (1.02), while there was zero incidence for B. cereus and hepatitis A.

In the Southeast, the highest incidence was related to the etiological agent rotavirus (1.74), and the etiological agent with the lowest incidence in the same period (2000–2010) was B. cereus (0.45). In the second period analyzed (2011–2021), the highest incidence of etiological agent in the Southeast was E. coli (0.84), and the lowest incidence was B. cereus (0.13).

In the South, the highest incidence was related to the Salmonella agent (2.08), while there were no cases (zero incidence) of hepatitis A for the period 2000–2010. In the second period analyzed (2011–2021), the highest incidence of etiological agent in the South was E. coli (1.09) and the lowest incidence was rotavirus (0.01).

The complete analysis of the time series demonstrated that the highest incidence of WFD cases was identified in the South region, which was not statistically different from the Southeast, despite demonstrating a higher average. The other regions presented lower rates than the South but did not show a significant difference from the Southeast region (Figure 2(a)).
Figure 2

Incidence of patients distributed across the five regions of the country (North, Northeast, Midwest, Southeast, and South) in the historical series from 2000 to 2021. Regions with the same letter did not show a statistically significant difference (p > 0.05). Factor (F; p): between groups (7.282; 3.25 × 10−5), demonstrating a significant effect of difference between the regions analyzed.

Figure 2

Incidence of patients distributed across the five regions of the country (North, Northeast, Midwest, Southeast, and South) in the historical series from 2000 to 2021. Regions with the same letter did not show a statistically significant difference (p > 0.05). Factor (F; p): between groups (7.282; 3.25 × 10−5), demonstrating a significant effect of difference between the regions analyzed.

Close modal

From the time series presented (Figure 2(b)), it is clear that the average trend was a reduction in the incidence of cases over the years. The South region maintained a more standardized trend in reducing cases, followed by the Southeast region. The North and Northeast regions showed the greatest discrepancy during the time series. In the case of the Midwest, there was the smallest variation over the years, maintaining a continuous incidence profile, without tending to point directly to a reduction.

The trend of etiological agents (n = 10) involved in cases of sick individuals shows that the main microorganisms reported in Brazil that cause diseases throughout the time series studied are Salmonella, rotavirus, E. coli, and Staphylococcus (Figure 2). These microorganisms were responsible for over 70% of infections, demonstrating high importance and an ongoing risk for the Brazilian population.

Regarding dietary pathways that result in infections, the bacteria E. coli showed a distribution pattern of infections in food significantly similar to Salmonella and Staphylococcus. However, the last two differed from each other and differed from the pattern of infections caused by rotavirus, which, in turn, did not demonstrate significant similarity to any of the other microorganisms.

Among the four etiological agents illustrated in Figure 3, the main means of transmission reported for E. coli (46.24%) and rotavirus (61.87%) was water. For rotavirus, represented in the ‘others’ classification, person-to-person contagion was also significant, exceeding 30% of records. In the case of Salmonella (39.91%), there was greater evidence of individuals being contaminated by eggs, and for Staphylococcus (61.93%), the main cause of infections was mixed and multiple foods.
Figure 3

Sick individuals (on a logarithmic scale), according to etiological agents (Staphylococcus, E. coli, Salmonella, and rotavirus), stratified by the main foods that were vehicles for infections and outbreaks of WFD (2000–2021). Fresh meat – beef, pork, and poultry; Others – açaí, alcoholic, and non-alcoholic drinks, sweets and desserts, sweeteners, spices, edible ice cream, fats and oils, fish, soy-based products, person to person, embedded meat products, chemicals, and food products for nutritional uses specials. Etiological agents followed by the same lowercase letter did not statistically differ from each other (p > 0.05), considering all disease-causing foods in the period from 2000 to 2021. Factor (F; p) – Agent (103.9; 2.47 × 10−53), Food (40.07; 2.33 × 10−50), and Interaction (16.98; 1.11 × 10−52). Both the ‘Agent’ and ‘Food’ factors, as well as their interaction, have a highly significant effect on the dependent variable, with practically zero p-values, indicating a strong rejection of the null hypothesis that there is no difference between the groups.

Figure 3

Sick individuals (on a logarithmic scale), according to etiological agents (Staphylococcus, E. coli, Salmonella, and rotavirus), stratified by the main foods that were vehicles for infections and outbreaks of WFD (2000–2021). Fresh meat – beef, pork, and poultry; Others – açaí, alcoholic, and non-alcoholic drinks, sweets and desserts, sweeteners, spices, edible ice cream, fats and oils, fish, soy-based products, person to person, embedded meat products, chemicals, and food products for nutritional uses specials. Etiological agents followed by the same lowercase letter did not statistically differ from each other (p > 0.05), considering all disease-causing foods in the period from 2000 to 2021. Factor (F; p) – Agent (103.9; 2.47 × 10−53), Food (40.07; 2.33 × 10−50), and Interaction (16.98; 1.11 × 10−52). Both the ‘Agent’ and ‘Food’ factors, as well as their interaction, have a highly significant effect on the dependent variable, with practically zero p-values, indicating a strong rejection of the null hypothesis that there is no difference between the groups.

Close modal
All of these product contaminations are spatially distributed based on the location of consumption of food and water. Both the inadequate food sterilization treatment in the process of obtaining and final preparation, as well as the ineffectiveness of conventional microbiological disinfection treatments for water by t conventional products, can facilitate the infection of consumers. Figure 4 shows the main sources of contamination reported in Brazilian WFD bulletins, taking into account the most representative microorganisms (n = 4).
Figure 4

Number of sick individuals on a logarithmic scale, according to etiological agents (Staphylococcus, E. coli, Salmonella, and rotavirus) and stratified by the site of contamination that served as the vehicle for infections and outbreaks of WFD (2000–2021). Others – cases scattered throughout the neighborhood, cases spread across more than one municipality, nursing home. Etiological agents followed by the same lowercase letter did not statistically differ from each other (p > 0.05), considering all disease-causing contamination sites in the period from 2000 to 2021 using the Tukey test. Factor (F; p) – Agent (38.77; 4.06 × 10−20), Locations (29.93; 4.48 × 10−36), and Interaction (5,194; 1.04 × 10−09). These results indicate that both the ‘Agent’ and ‘Location’ factors have significant effects and that the interaction between these two factors is also significant.

Figure 4

Number of sick individuals on a logarithmic scale, according to etiological agents (Staphylococcus, E. coli, Salmonella, and rotavirus) and stratified by the site of contamination that served as the vehicle for infections and outbreaks of WFD (2000–2021). Others – cases scattered throughout the neighborhood, cases spread across more than one municipality, nursing home. Etiological agents followed by the same lowercase letter did not statistically differ from each other (p > 0.05), considering all disease-causing contamination sites in the period from 2000 to 2021 using the Tukey test. Factor (F; p) – Agent (38.77; 4.06 × 10−20), Locations (29.93; 4.48 × 10−36), and Interaction (5,194; 1.04 × 10−09). These results indicate that both the ‘Agent’ and ‘Location’ factors have significant effects and that the interaction between these two factors is also significant.

Close modal

While bacteria tend to maintain similar behavior, with housing/work being the greatest disseminator of the disease, rotavirus presented a greater number of cases dispersed throughout the municipalities. Statistical tests revealed that E. coli did not differ statistically from Salmonella and Staphylococcus, while the latter two reported a statistical difference between them regarding the number of patients on different infection sites. In contrast, rotavirus differs statistically regarding the distribution of the number of patients caused by E. coli, Salmonella, and Staphylococcus.

The majority of notifications registered by the Brazilian health system corresponded to infections caused by bacteria, followed by viruses. This result demonstrates behavior contrary to that observed in the USA, according to the Centers for Disease Control and Prevention, which reports that around 64% of infections are caused by viruses and 15% by bacteria. For the other classes of etiological agents and toxic factors, the results were similar, representing around 3% of cases in both Brazil and the USA (CDC 2022).

In this sense, disparities in the incidence of diseases attributable to the classes of bacterial or viral etiological agents can be influenced by a series of complex factors. These elements include epidemiological characteristics, health systems, public health policies, hygiene practices, climate, and even cultural and social traits (Wolf et al. 2014; Burrell et al. 2017).

According to Sarno et al. (2021), the European system for monitoring foodborne outbreaks is an example of the application of these concepts based on the One Health approach, which now provides greater security for the entire European Union. In the case of the Brazilian context, one of the fundamental reasons for the predominance of diseases related to bacteria is linked to socioeconomic conditions and insufficient basic sanitation infrastructure (Ikhimiukor et al. 2022).

According to Abebe et al. (2020), bacteria are the causative agents of two-thirds of foodborne human diseases worldwide, with a high burden in developing countries. Thus, the lack of access to adequate basic sanitation, drinking water, and the existence of precarious living conditions in certain areas of Brazil can amplify the spread of diseases of bacterial origin.

Regarding the main microorganisms causing WFD presented in this research (Figure 1), eight of them were also reported in the study carried out by Lee et al. (2021) in a 5-year historical series on WFD in South Korea, with only rotavirus and the coliform group not being directly cited by the authors.

A study carried out by Park et al. (2018) points out that the incidence of foodborne diseases is also interrelated with climatic variables. This research demonstrated that climate characteristics influence the transmission of enteric diseases, such as those caused by E. coli and Salmonella. Both microorganisms mentioned were also precursors of most of the infections in our analyses.

Most regions followed the general trend in relation to the percentage of each etiological agent. However, there was a notable exception in the Southeast region, which presented a greater number of diseases caused by viruses (these viruses include rotavirus, norovirus, and hepatitis A virus) than by bacteria. This data are particularly relevant, since the Southeast region is the most populous and developed in the country, and has a relatively high population density compared to other regions. This fact corroborates the behavior of the classes of infectious agents in the USA, which also has a higher number of reported viral outbreaks, showing itself to be different from all other Brazilian regions (CDC 2022).

Taking into account that the indicators (health, economy, and basic sanitation) related to the causes of WFD tend to improve over the years, the incidence should suffer reductions (IBGE 2021; Brasil 2023). However, there may still be a gap in underreporting by the health system, which should lead to an increase in the number of cases and incidence due to better coverage of care in the health system.

The study carried out by Contreras et al. (2022) demonstrates that, in areas of high population density, a greater sanitation coverage generates a reduction in fecal contamination, which improves child health rates. This means that, although population clusters favor the high transmission of diseases, when they are controlled in sanitary terms, they can result in greater control of public health.

Ketola et al. (2021) reported that the dynamics of villages and families, in relation to the structure and population of communicable diseases, have an effect on epidemics and infectious outbreaks and can vary depending on the etiological agent and population size, which make it difficult to generalize the consequences of epidemics, especially in larger populations or with different organizational structures.

This difference in behavior between regions may be influenced by the proficiency of health, education, and population care campaigns. Locations with greater coverage of health units facilitate access for the population, who are beginning to frequent these spaces for routine monitoring. This makes the possible occurrence of outbreaks more predictable, which also favors the action of authorities, so that infections do not escape control, affecting a larger population (Todd 2020; Damini 2023).

Furthermore, there is also a strong influence of the factors of sewage collection and treatment, solid waste management, and access to water supply on the WFD profile. According to the National Sanitation Information System (SNIS), historically, in Brazil, the North and Northeast regions have the lowest level of service in these three basic sanitation parameters (Brasil 2023), which can cause instability and unpredictability in the spread of pathogens causing waterborne and foodborne outbreaks.

In this study, Salmonella, rotavirus, E. coli, and Staphylococcus were responsible for over 70% of infections. These data are in agreement with other research (Curtis et al. 2014; Dennehy 2015; Dewey-Mattia 2016; Wittler 2023), which indicates the high pathogenic and infectious potential of these etiological agents.

Diseases transmitted by water and food occur through ingestion when contaminated with microorganisms or chemicals. Contamination risks exist in the food chain from food production to consumption (‘farm to fork’) and involve water, soil, or air pollution (Hald et al. 2016; Cissé 2019).

Therefore, despite there being separation between the means of transmission, there is a direct correlation between the quality of water and food. This has been demonstrated to the extent that ensuring the microbial quality of irrigation water is critical to reducing product-related foodborne outbreaks and thus increasing food security (Morris et al. 2011).

An indicator of risk related to bacteria also involves the ability of these microorganisms to survive in environments with variations in temperature, pH, and few nutrients. Studies demonstrate the ability of E. coli to survive with low amounts of nutrients, which may be linked to its ease of transmission by waterborne and subsequent infection in humans (Cook & Bolster 2007).

According to Liu et al. (2018), Salmonella ranks high among the pathogens that cause outbreaks of foodborne illnesses. In line with the data from this work, Sivapalasingam et al. (2004) and Hanning et al. (2009) reported that the main source of salmonellosis has been attributed to the ingestion of meat, eggs, and other poultry products. Salmonella is estimated to cause 93.8 million cases of gastroenteritis worldwide annually, with 155,000 deaths (Majowicz et al. 2010). In the USA, non-typhoid Salmonella is among the leading causes of gastroenteritis related to food consumption. It is estimated that approximately one million people become infected annually, resulting in medical costs of US$3.7 billion (Morris et al. 2011).

Staphylococcus aureus is an important microorganism that causes food poisoning worldwide, accounting for about 20–25% of foodborne bacterial outbreaks in China (Wang et al. 2014). This microorganism, according to Li et al. (2019) and Alghizzi & Shami (2021), can be associated with the contamination of dairy products, sushi, meat, and other foods. Furthermore, it is the leading cause of infection in both healthcare facilities and communities, causing illnesses ranging from mild infections to life-threatening illnesses (Song et al. 2015).

In addition to bacteria, viruses are organisms capable of spreading and being transmitted through environmental routes, such as air, inert surfaces, and liquid media. It is important to highlight that most viruses involved in liquid transmission pass through water (Pinon & Vialette 2018). Waterborne transmission of viruses occurs easily, especially in cases of contact with untreated effluents, where the microbiological load is high. The transmission of pathogens is not limited to direct person-to-person contagion and can be linked to human activities, including the disposal of untreated sewage, reuse of poorly treated effluents, and the use of animal waste as manure (Haramoto et al. 2018; Lanrewaju et al. 2022).

These and other sources are linked to the consumption of drinking water for irrigation, aquaculture, recreational activities, and other activities. Virtually all forms of water are subject to viral contamination (Habib et al. 2021). Rotavirus, norovirus, hepatitis A virus, adenovirus, and other related enteric viruses are considered one of the main causes of diarrhea and waterborne diseases worldwide, in addition to presenting a high mortality rate in children under 5 years of age. Despite vaccination campaigns, mainly in low-income countries, around 200,000 deaths are reported annually caused by rotavirus infections (Crawford et al. 2017; Kittigul & Pombubpa 2021).

As well as the analysis related to dietary factors that led to the causes of infection, a different dynamic was also observed in relation to the places where the majority of patients were reported, which varied according to the microorganism analyzed.

The investigation and identification of contamination sites, as well as their etiology, are essential components in the control of WFD, since outbreaks without clarification of the causes generally result in late notification or even underreporting (Eduardo et al. 2003; Oliveira & Ferreira 2021). School and daycare environments are often reported as places where outbreaks occur. According to a study carried out by Faúla et al. (2015), the school environment was responsible for the second place, in Minas Gerias, with the highest occurrence of foodborne disease outbreaks between 2010 and 2014.

Another environment related to a large number of WFD cases is homes. Eduardo et al. (2003) and Batista et al. (2022) point out the hygienic habits of food handlers, the use of poorly sanitized utensils, inadequate food preparation and storage as the main causes of household outbreaks. Event locations and industrial cafeterias are environments that concentrate a large number of people, and are becoming significant places due to the large number of exposed people involved (Barreto & Sturion 2010). Similar results were found in the analysis of foodborne disease outbreaks in the state of Rio Grande do Sul by Welker et al. (2010), which identified homes as the main place of occurrence of the investigated outbreaks, followed by commercial establishments and company cafeterias.

Additionally, Figure 4 presents 10 classifications for the location of the outbreak. Although these classes presented in Figure 4 cover a diversity of environments, there is still a generalization that may leave out other places susceptible to contamination, such as beaches that present annual outbreaks of infections in Brazil. In comparison, the United States national outbreak reporting system (CDC 2022), available on the outbreak data portal, uses 71 different classifications. This approach allows a greater number of categories to be covered, enabling a more detailed analysis of the epidemiological profile of each etiological agent.

The main limitations of this study, despite the conclusions presented, were the high percentage of ‘inconsistent, inconclusive or ignored’ data, which was a problem for preparing action plans and reports that map the main epidemiological risks. In addition to the complete historical series, the reduction in notifications in the second period (2011–2021) presents strong evidence of underreporting, as reported in other studies (Araújo et al. 2021; Oliveira & Ferreira 2021; Batista et al. 2022). Especially in the last 2 years (2020 and 2021), this factor may have been even more pronounced, taking into account the Covid-19 pandemic. Furthermore, the Ministry of Health's database does not account for cases reported on beaches, which are public places, annually reported in the media as hotspots for high infection rates in Brazil.

Furthermore, it is estimated that only 5–10% of WFD outbreaks are reported to the responsible bodies (Oliveira & Ferreira 2021). This highlights the need to automate notification and training systems for the different sectors involved in this process. It should also be noted that the notifications contain data on cases evaluated in health units, which obviously disregards those who did not have access to or did not seek medical treatment. Therefore, it is important that in future work, statistical methods capable of measuring the real number affected by WFDs are developed.

This study, which evaluated the 22-year historical series on Food and Waterborne Diseases (WFD) in Brazil, revealed significant insights and persistent public health concerns. The regional heterogeneity identified, with the Southeast standing out for the prevalence of viral diseases and the other regions dominated by bacterial infections, highlighting the complexity of the epidemiological panorama of WFD in the country. Notably, E. coli has emerged as the most prevalent pathogen in recent years, potentially reflecting changes in environmental or public health factors.

Considering the critical role of water as a transmission vehicle, along with food, we reiterate the need to reinforce surveillance and quality control systems. The presence of inconsistent or inconclusive records in more than 50% of notifications points to a significant gap in health systems, limiting more accurate analyses and effective preventive actions.

This investigation provides a robust basis for future research, suggesting further investigation into the correlation between the incidence of WFD and the factors that influence it. An in-depth understanding of these relationships is essential for implementing more targeted and effective public policies aimed at preventing and controlling these diseases. Furthermore, the importance of improving the accuracy and comprehensiveness of disease reporting systems is emphasized, which is crucial for a more effective public health response and population safety.

Conception and project design were led by U.A.B., G.M.R., and K.A.F. U.A.B. was responsible for writing the article. The methodology, validation, investigation, and formal analysis were conducted by U.A.B., G.M.R., K.A.F., V.S.C., and J.A.L. Data curation was managed by U.A.B., K.A.F., and J.A.L. The review and editing process involved U.A.B., G.M.R., K.A.F., V.S.C., J.A.L., and J.C.M.

All relevant data are available from Harvard Dataverse: Dataset “Waterborne and Foodborne Diseases - WFD - Brazil (2020 - 2023)”: https://doi.org/10.7910/DVN/LGRXIV.

The authors declare there is no conflict.

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