Next Article in Journal
What Makes Chinese Adult Children Behave Differently during Parents’ End of Life: A Discriminant Analysis of Macao Chinese
Previous Article in Journal
Community Pharmacists’ Perceptions, Barriers, and Willingness for Offering Sexual and Reproductive Health Services
Previous Article in Special Issue
Estimation of the Impact of Foodborne Salmonellosis on Consumer Well-Being in Hungary
Article

Risk Factors for Positivity to Shiga Toxin-Producing Escherichia coli and Salmonella enterica in Backyard Production Systems Animals from Metropolitana Region, Chile: A Threat to Public Health?

1
Departamento de Medicina Preventiva Animal, Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago 8820808, Chile
2
Central Veterinary Research Laboratory, Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago 8820808, Chile
3
Facultad de Ciencias Agropecuarias y Ambientales, Universidad del Alba, Santiago 8370007, Chile
*
Author to whom correspondence should be addressed.
Academic Editor: Beate Conrady
Int. J. Environ. Res. Public Health 2021, 18(20), 10730; https://doi.org/10.3390/ijerph182010730
Received: 1 September 2021 / Revised: 11 October 2021 / Accepted: 11 October 2021 / Published: 13 October 2021
(This article belongs to the Special Issue Epidemiological, Mitigation and Economic Impact of Zoonoses)

Abstract

In the Metropolitana region of Chile there are 3836 backyard production systems (BPS), characterized as small-scale systems. They act as a source of zoonotic pathogens, such as Salmonella enterica and Shiga toxin-producing Escherichia coli (STEC), whose prevalence in BPS has not been fully described. The objective of this study was to determine the positivity for both agents in BPS and to establish the risk factors related to their presence. In each BPS, an epidemiological survey was undertaken, and stool samples were collected to detect these pathogens via bacteriological culture and conventional PCR techniques. Subsequently, multivariable logistic regression models were applied to establish the risk factors associated with their presence. BPS positivity rates of 11.76% for STEC and 4.7% for S. enterica were observed. The systems showed poor welfare standards and a lack of biosecurity measures. The risk factor analysis concluded that the Gini–Simpson index (p = 0.030; OR = 1.717) and the presence of neighboring intensive poultry or swine production systems (p = 0.019; OR = 20.645) act as factors that increased the risk of positivity with respect to STEC. In the case of S. enterica, exchanging embryonated eggs (p = 0.021; OR = 39) and the presence of debeaked chickens (p = 0.001; OR = 156) were determined as factors that increased the risk of positivity for this agent. For positivity with respect to both pathogens, the Gini–Simpson index (p = 0.030; OR = 1.544) and being INDAP/PRODESAL users (p = 0.023; OR = 15.026) were determined as factors that increased the risk, whereas the type of confinement (p = 0.002; OR = 0.019) decreased it. Epidemiological surveillance of these neglected populations is lacking, highlighting the fact that STEC and S. enterica maintenance on BPS represents a potential threat to public health.
Keywords: backyard production systems; STEC; Salmonella enterica; risk factors; public health; One Health; zoonosis backyard production systems; STEC; Salmonella enterica; risk factors; public health; One Health; zoonosis

1. Introduction

1.1. Backyard Production Systems

Backyard production systems (BPS) are considered one of the most common forms of animal production in the world, especially in developing countries [1]. BPS are small home-based production systems with low technological development, located mainly in rural areas [2,3,4]. Although they are small-scale systems, BPS constitute part of the household income through the domestic consumption of animal protein and subproducts and the informal sale of organic farm products; a market that has gained popularity in the last few years as a result of the new food preferences of consumers [5,6]. In this regard, BPS have been found to be socioeconomically valuable in various rural communities around the globe, including Chile [7,8,9].
One of the main characteristics of BPS is their lack of biosecurity measures [7,10] and their limited use of veterinary assistance, which may limit the early detection of infectious diseases caused by zoonotic pathogens [4]. Additionally, a high rate of contact between the different animal species kept in BPS can be observed [6], leading to the maintenance and dissemination of a wide range of pathogens [11,12,13]. Among these are enteric bacteria such as STEC and S. enterica; zoonotic pathogens considered as two of the main causes of foodborne illness globally [14,15,16,17]. A total of 3 million acute cases, 3890 cases of hemolytic uremic syndrome (HUS) and 230 deaths per year worldwide are estimated to be due to STEC [18]. For non-typhoidal salmonellosis, around 535.000 acute cases and 77.500 deaths are estimated to have occurred in 2017 [19].

1.2. Shiga Toxin-Producing E. coli and Salmonella enterica

Both E. coli and S. enterica are Gram-negative, facultative anaerobic bacilli; members of the Enterobacteriaceae family, which are the most abundant enteric commensal and pathogenic agents in homeothermic animals [20,21]. E. coli can be classified in eight pathotypes, with STEC being one of the zoonotic pathotypes [22,23]. The characterization of STEC is based on the detection of the genes encoding for Shiga toxins (Stx): Stx1 and Stx2 with their respective subtypes [24]. In the case of S. enterica, more than 2650 serotypes of S. enterica have been described [25].
Both pathogens can cause asymptomatic infections or self-limiting gastrointestinal signs [26,27], but they can also cause a severe clinical presentation, extraintestinal disease or even death in the at-risk population, including immunocompromised patients, children, pregnant women and seniors [27,28]. STEC is mainly associated with diarrhea, which can be hemorrhagic. In addition, it can produce HUS in the at-risk population [21], which can be life-threatening or can lead to renal, gastrointestinal, cardiovascular or central nervous system complications, especially in children [29]. S. enterica normally causes self-limiting diarrhea, but can also generate severe disease and can progress to bacteremia with extraintestinal complications [27], including pneumonia, cholecystitis, cellulitis, pancreatitis, urinary tract infections, appendicitis, endocarditis and meningitis [30].
The horizontal route is the most important for the transmission of both agents. Transmission via the oral–fecal route or consumption of water or food contaminated with waste or feces, represent the main sources of infection for humans [30,31]. Both pathogens are frequently isolated in cases of foodborne diseases [14,15,16,17], with detection mainly on poultry carcasses, eggs, dairy products and beef [32].
The role of several animal species as a reservoir for these pathogens has been previously described, including wild and domestic species such as rodents, reptiles, poultry and wild birds, dogs, cats and pigs [32,33]. Cattle are recognized as one of the largest reservoirs of STEC [21], whereas hens and swine are the most important reservoirs of S. enterica [34]. Animal reservoirs are generally asymptomatic, although young animals may become ill [29]. BPS animals can contaminate food or watercourses, infecting other animals and potentially transmitting these pathogens to humans [30,35].
Worldwide, and particularly in Latin America, the S. enterica and STEC prevalence in BPS is largely unknown. In cattle, the prevalence of STEC O157 is reported to range from 0 to 71%, and in some herds the infection rate is as high as 100% [36]. The prevalence has been described as ranging between 0.2 and 74% in dairy cattle [36,37], as over 70% in sheep and goats [38] and as 4% in captive wild birds [39], with several other reports of its presence in other animal species that have close contact with humans [40,41]. In contrast, the prevalence of S. enterica at the BPS level is reported to range from 3.5% to 31.0% in hens reared under these low-tech production systems [42,43], demonstrating that these animals are not part of any surveillance system in the region.
In animals, limited risk factors have been described for STEC, including low levels of technification. Calf groups have been reported as factors that increase the prevalence in cattle [44]. Risk factors for positivity with respect to S. enterica in BPS have been reported only to a limited extent, due to major gaps in knowledge regarding this high-risk population. Larger flocks of chickens, the number of other bird species (e.g., geese, turkeys or ducks), free-range birds, BPS mixed production and mixing replacement animals all lead to a significantly higher risk of positivity with respect to Salmonella. Implementing any action when the disease was present led to a significantly lower risk of becoming Salmonella-positive [6,42].

1.3. Chilean Situation

Information on the sanitary status of both bacteria in BPS is scarce [1], partly because their clinical presentation is mostly asymptomatic in animals [45]. The prevalence of S. enterica has been reported to be between 4.2 and 21.23% in hens and swine bred under BPS in central Chile [6,46,47]. For STEC, a recent study reported positivity rates of 17% in cattle and 1% in pigs, where both species were from commercial farms [48]. In addition, the following factors are described as factors that increase the risk of positivity for S. enterica in BPS: bird species diversity (odds ratio (OR) = 1.04, 95% CI: 1.01–1.07), BPS with mixed production activities (agricultural and livestock) (OR = 5.35, 95% CI: 1.2–27.6) and BPS that obtained replacement animals from various sources (OR = 5.19, 95% CI: 1.4–20.5) compared to those using their own animal replacements [6].
No information has been reported in relation to the prevalence and risk factors for STEC in BPS in Chile to date. For S. enterica, even when national evidence has been reported, the information is limited in terms of risk-factor determination. This highlights the great gap in the information available regarding both bacteria in BPS animals, which could lead to an increase in the risk of transmission, presenting a threat for animal and/or public health.
Based on all the above, it is necessary to increase the existing information regarding these two bacteria and their presence and behavior in BPS. The aim of this study was to determine the risk factors associated with positivity for both bacteria in terms of management, biosecurity aspects, sanitary status and relationship with the environment, by characterizing these production systems and establishing their positivity rates for S. enterica and STEC, assuming the circulation of these two important bacteria among animals raised in BPS.

2. Materials and Methods

2.1. Study Design and Sample Unit Determination

Stratified and proportional random sampling was performed, based on the provinces within the Metropolitana region (Chacabuco, Cordillera, Maipo, Melipilla, Santiago and Talagante). The sample size was calculated using the following equation [49]:
n = Zα2pq/L2
where n represents sample size and Zα is the required value for confidence = 1 − α, where α corresponds to the confidence level set at 95%, Zα is the (1 − α/2) percentile of a standard normal distribution, p is the expected prevalence of the pathogen, q = 1 − p and L is the precision of the estimation or margin of error set at 5%. Based on previous studies of the epidemiology of S. enterica in BPS in the Metropolitana region, the sample size was calculated using a prevalence of 5% [6]. BPS that maintained at least one productive animal species (mainly poultry and swine) were selected. Based on the above and on the information from the last Animal and Forestry census carried out by the Instituto Nacional de Estadísticas de Chile (INE) [50], a sample size of 73 BPS was determined (Table 1).
The intra-BPS sample size was calculated using the following equation [49]:
n = (1 − α1/D)(N − (D − 1)/2)
where n represents the sample size, N is the sample size of BPS, D is the minimum estimated number of sick animals in the group and α = 1 − the confidence level. Considering the detection of at least 30% of positive animals, a minimum sample size of 8 animals in each BPS was determined. In those cases where it was not possible to capture the minimum number of animals to sample, fresh environmental samples were collected.

2.2. Epidemiological Data Collection

During the field activities, a previously validated [6] survey was applied in each BPS (applied questionnaire in Supplementary Material), in order to characterize the factors that may influence the maintenance and dissemination of the pathogens under study, including handling, the presence or absence of specific biosecurity conditions, socioeconomic variables and animal exchange. The respondent’s names, as well as the georeferences, were codified and blinded for the research group. Only the PI had access to the information in full detail. The interviews were conducted in Spanish by the corresponding author of this manuscript, who has been trained in epidemiologic questionnaire application. None of the surveyed BPS owners had any relationship with any member of the research team and all survey respondents were over 18 years old. Each interview lasted an average of 20 minutes. Survey questions were aimed at poultry and swine breeding, since these are the most common species reported as being kept in BPS. The information was manually collected and then digitized, processed and managed using Microsoft Excel® (version 16.50). Informed consent was obtained to ensure that both the information collected through the survey and the results of the analysis of the sample were treated confidentially, and to allow the publication of global project results while protecting personal data. The questionnaire was approved by the bioethics committee of Universidad de Chile, under the number 18205-VET-UCH. All BPS owners were informed about the results of the samples collected from their animals.

2.3. STEC and S. enterica Positivity Establishment

Fecal samples were collected from animals raised in BPS, taken directly from the cloaca in birds or from the rectum in mammals, using sterile swabs with Cary–Blair transport medium (Copan®, Italy). Additionally, environmental samples were collected only under the following conditions: the presence of fresh feces and species identification. Samples were transported and stored at 4 °C until processing.
For STEC detection, protocols for bacteriological cultures described previously were followed [32,51]. The STEC culture protocol includes: pre-enrichment of the samples with 5 mL of trypticase soy broth (Becton, Dickinson and Company, Franklin Lakes, NJ, USA), incubated at 42 °C for 18 to 24 h, followed by enrichment and isolation of a homogeneous aliquot on MacConkey Agar (Becton, Dickinson and Company, Franklin Lakes, NJ, USA), incubated at 37 °C for 18 to 24 h. Those plates showing bacterial growth were confirmed using conventional PCR techniques. The presence of Stx1 and/or Stx2 genes was assessed via PCR with primer sets and reaction conditions following protocols previously described [51].
The protocol for S. enterica isolation consisted of three stages: pre-enrichment of samples with 5 mL of phosphate buffered peptone water broth (APT, Difco®), incubated at 37 °C for 24 h, followed by plate culture with modified semi-solid Rappaport–Vassiliadis medium (MSRV, Oxoid®), incubated at 42 °C for 24 to 48 h. Finally, those plates showing growth were isolated on plates with xylose lysine deoxycholate (XLD, Difco®) selective medium, incubated at 37 °C for 24 h. Plates with concentric black or red colonies, without color change, were confirmed using conventional PCR techniques, searching for the invA gene according to previously described procedures [52].

2.4. Risk Factor Determination

Since the response variable (positivity to STEC and/or S. enterica) was dichotomous, where Y can take only two values, 0 or 1 (Y = 0 or Y = 1), representing the absence (0) or the presence (1) of the studied agent [49,53], three multivariable logistic regression models were built: one for STEC, one for S. enterica and one that grouped both pathogens (called the Enterobacteria model) [49,54,55].
For the selection of variables to be analyzed as potential risk factors, a univariable logistic regression analysis was performed for all the variables reported in the survey, and those with a p-value of less than 0.15 (liberal p-value criterion) were selected [49]. A cut-off value of p < 0.20 was also evaluated, showing no difference in the variable selection. All the variables that fulfilled this criterion were analyzed using Spearman’s correlation for quantitative variables and Fisher’s exact test for qualitative variables, to check the collinearity and the association between variables, allowing correction for potential confounding factors.
The multivariable logistic models were built using the following equation [49,53]:
l n ( p / 1 p ) = β 0 + β j X j  
where p is the probability of the outcome at any value of x, β0 corresponds to the intercept and ∑βjXj is the summation of the effect (βj) of each independent variable (Xj) added into the model [54].
The construction of the multivariable model was subjected to a stepwise backward elimination procedure, removing from the model those variables that presented non-significant regression coefficients (p > 0.05) when the models were compared using the likelihood ratio test (LRT) [54]. Those variables that were not significant for the construction of the model and which, when eliminated, modified the regression coefficients of the remaining variables by more than 20%, were retained in the final model, to adjust for confounding factors. The convergence of each model was set at epsilon (ε) = e−16, in order to present stricter conditions for determining statistically significant factors. The final model was the one that presented the lowest record in the LRT [54]. The goodness of fit was assessed using the Hosmer–Lemeshow test [54,56].
In order to have a variable that takes account of the diversity of species present in BPS as a potential risk factor, the Gini–Simpson index was calculated [57]. In addition, interactions in a biological and/or epidemiological sense were included between the evaluated variables.
All the analyses were performed using the statistics software R version 4.1.0 [58] and RStudio [59].

3. Results

3.1. Characterization of the Sampled BPS

A total of 85 BPS were sampled in the Metropolitana region. When broken down at the province level, a total of 34 BPS were sampled in Melipilla, 13 in Chacabuco, 10 in Santiago, 5 in Cordillera, 7 in Talagante and 16 in Maipo. An average of 8.38 samples were collected in each BPS. The epidemiological survey showed that, on average, families consisted of four or five members. When asked about their production systems, 51.76% (44/85) of owners reported having a mixed production, shared between agriculture, forestry and livestock. Regarding the occupations of BPS’ owners, 36.47% (31/85) declared themselves to be pensioners, 37.64% (32/85) agricultural workers and 25.88% (22/85) non-agricultural workers. Owners were also asked to categorize the importance of animal breeding for the household economy on a scale of 1 (not important) to 5 (very important). In general, each category presented relatively homogeneous frequencies (1 = 16/85, 2 = 12/85, 3 = 16/85, 4 = 25/85 and 5 = 16/85).
The BPS visited were composed of a great diversity of species, including domestic birds, large and small ruminants, other herbivores, swine and pets (Table 2). The median obtained for the total number of animals in each sampled BPS was 40 animals. From this data, the Gini–Simpson diversity index was calculated for each BPS.
In terms of BPS characterization, animal management was carried out mostly by women (36/85), followed by men (23/85) and families (22/85). Most BPS owners reported that they had kept and bred animals, with the aim of domestic consumption and to sell their products, for more than twenty years. The animals were generally kept under mixed confinement. Despite having functional fences, contact was possible between different species in the BPS and also with neighboring BPS animals, wildlife and visitors to the BPS. Replacement animals mainly originated from the same system or, to a lesser extent, from multiple sources including fairs and neighboring farms. Very few BPS owners claimed to exchange embryonated eggs or keep debeaked chickens, and none of them kept tail-docked swine. Most BPS did not receive veterinary assistance; the owners dealt with management and administering drugs or other treatments to sick animals themselves. In addition, relationships between BPS owners and governmental entities in charge of providing support to this type of system were scarce. Further information on BPS characterization is given in Table 3.

3.2. Positivity to STEC and S. enterica

Of all the BPS analyzed, ten were positive for STEC and four were positive for S. enterica, representing positivity rates of 11.76% and 4.71%, respectively (Table 4). With regard to to the species involved in STEC positivity, ruminants (cows and sheep) were the most common species found to be positive for this agent, followed by poultry (chickens and ducks) and pigs (Table 5). In the case of S. enterica, birds (chickens and geese) were the main group to show positivity for this agent. A total of 712 samples were collected, obtaining 20 (2.81% sample positivity) STEC-positive samples, with 80% positive for Stx1 (16/20) and 40% positive for Stx2 (8/20), together with 5 (0.70% sample positivity) S. enterica-positive samples (Table 5).

3.3. Risk Factors for STEC Positivity

Regarding the constructed epidemiological model, the variables that were shown to be statistically associated with positivity with respect to STEC were the Gini–Simpson index (OR = 1.717; CI-95%: 1.054–2.799; p = 0.030) and the presence of neighboring intensive poultry or swine production systems (OR = 20.645; CI-95%: 1.648–258.706; p = 0.019). Both acted as factors that increased the risk of positivity (Table 6).

3.4. Risk Factors for S. enterica Positivity

The significant variables in the epidemiological model for S. enterica positivity were the exchange of embryonated eggs, behaving as a factor that increased the risk of positivity (OR = 39; 95% CI: 1.745–871.724; p = 0.021) and the presence of debeaked chickens, also being a factor that increased the risk of positivity (OR = 156; 95% CI: 6.979–3486.896; p = 0.001) (Table 7).

3.5. Risk Factors for STEC and S. enterica Positivity

The model obtained for both bacteria had the following significant variables: the Gini–Simpson index, the type of confinement under which the animals were kept and whether the BPS was a user of the Instituto de Desarrollo Agropecuario (INDAP) or Programa de Desarrollo Social (PRODESAL). Both the Gini–Simpson index and whether the BPS was a user of INDAP or PRODESAL, were factors that increased the risk for positivity to both pathogens (OR = 1.544; 95% CI: 1.044–2.284; p = 0.030 and OR = 15.026; 95% CI: 1.465–154.082; p = 0.023, respectively). In contrast, the type of confinement was a factor that decreased the risk for positivity to the studied agents (OR = 0.019; 95% CI: 0.001–0.244; p = 0.002), in cases where the animals were kept under a mixed confinement system (Table 8).

4. Discussion

4.1. Economic Perspective of BPS in the Chilean Context

BPS are important for the community, because they organically produce foods of animal origin, which, according to current food trends, are preferred by consumers [5]. In general, the BPS characterization in this study reported similar results to those observed previously, both in Chile and in the rest of the world, highlighting a great diversity of intra-BPS species destined for domestic consumption or sale, kept using low biosecurity measures with little veterinary assistance, where such production was not the main economic activity of the household [3,4,6,7]. It should be noted that the importance of animal breeding for the household economy varied considerably between BPS. In this sense, animal production can be presented as the main or a secondary business, implying that at least a significant proportion of BPS owners must perform other jobs or activities in addition to animal breeding, to maintain the household economy. This situation limits the amount of time available for BPS management, thus increasing biosecurity deficits [60]. BPS and integrated production systems are key to promoting the local economy in undeveloped and developing countries [61].

4.2. BPS Management Characterization

Characterization showed that STEC- and S. enterica-positive BPS owners do not normally associate with, or receive veterinary advice from, official entities such as SAG, INDAP or PRODESAL (veterinary or agricultural advisory institutions belonging to the government (central and/or regional)), nor from private professionals (veterinarians or veterinary technicians). In addition, when a relationship between the BPS and the veterinary services exists, it is generally associated with swine breeding, due to the higher technical requirements for their maintenance and the existence of diseases that affect this species and are under official surveillance plans (e.g., PRRS, avian influenza and swine influenza). However, the number of BPS owners who report keeping pigs is low, which is reflected in the low rate of technical and health assistance that the surveyed BPS received. The absence of technical–sanitary assistance and the scarce implementation of biosecurity measures could increase the maintenance of zoonotic and non-zoonotic pathogens in BPS, with the consequent risk of contagion for the people in charge of these systems and the susceptible animal species kept in them or in neighboring systems (which could include small-scale and large-scale production systems) [62,63]. Only five BPS, of all those sampled, were visited by SAG more than once. This low frequency of visits could be an indicator of good health status, although, based on the observed handling and biosecurity characteristics, this situation is more likely to be explained by other factors. To make a more accurate estimate, it would be necessary for the BPS present in this region to be integrated transversally into the surveillance programs carried out by official entities [64,65,66,67].
Among the studied variables associated with biosecurity measures in BPS, the presence of a footbath and the practice of disinfection before entering and leaving the farm could not be included in the risk factor analysis, because the results did not show variability since there was no presence of the aforementioned protocols in any of the sampled BPS. This situation was corroborated by field inspection. This reinforces the hypothesis that BPS are themselves a promoting factor for the contact of zoonotic pathogens with the population. One example is the handling of animals while wearing clothes or footwear common to the rest of the daily activities, without rigorous disinfection or sanitation. These elements can then act as fomites, with the potential to infect other people or disseminate pathogens to other systems that may be visited by the BPS owners [60,68].
Another important characteristic to highlight is that, of the surveyed BPS, those in charge of maintaining them were mainly pensioners (older than 60 years); a situation that has been reported previously [7]. In Chile, this could be a factor that influences the lack of, or delay in, the adoption of new practices that could improve the sanitary status of these productive systems [69]. There is also a perception of mistrust in those in charge of BPS directed towards official veterinary and health authorities, which is reflected in the low participation rate of these entities in the visited BPS. An explanation for this may be that these organizations play an active role mainly against outbreaks of diseases of importance to commercial farms, such as avian influenza or PRRS, where the most common control measure involves the elimination of the affected animals [7,70]; an act that is not usually accompanied by financial compensation and therefore has an impact on the household economy [70].
Despite the fact that BPS owners reported infrequent use of pharmacological treatments, the administration of pharmaceutics to treat animals without a medical prescription was observed; an important situation since there is previous evidence of the presence of antibiotic residues in Chilean BPS [71]. Hence, the administration of pharmacological treatments could be underestimated, since at the time of sampling boxes and bottles of medicines such as antibiotics and analgesics were observed around pens. Additionally, there was a lack of knowledge about the concept of “vaccination”, with owners understanding it as the injection of any kind of drug or product. This fact may be an indirect indicator of a possible underestimation or overestimation of immunity levels for certain diseases [72,73]. The consequences of these practices could have an impact on public and animal health, mainly associated with the generation of antimicrobial resistance against widely used drugs [34,35,48,71,74,75]. Previous studies have detected multidrug resistance in non-typhoidal Salmonella strains from Chilean BPS [76] and sensitivity to a wide range of antimicrobials in STEC isolates from backyard animals [75].

4.3. STEC and S. enterica Positivity in BPS

Recent reports in Chile indicate STEC positivity rates of 17% in cattle and 1% in slaughtered pigs [48]. In addition, reports on meat, seafood, vegetables and ready-to-eat street-vended food samples indicate 0.5% positivity (18/3300) [77]. However, the results obtained for STEC in this study are the first reported at BPS level in Chile. This is important, given the potential risk to public health mainly associated with close contact between animals and people who are in charge of these systems, and the possible risk of occupational disease. Human–animal contact could be even higher than in commercial farms, where biosecurity measures are also stricter [68].
The positivity rates for S. enterica reported in this study are similar to those previously described for BPS in Chile [6,46] and in other countries [42,43,78,79]. In terms of the risk factor analysis, evidence suggests that those variables that were found to be significant and increased the risk of positivity with respect to S. enterica in this study were also found to be significant in previous studies [6].
The animal species that showed positivity to STEC and/or S. enterica were consistent with the reservoirs previously described in the literature for both pathogens [21,32,33,37].

4.4. Risk Factors Associated with STEC and S. enterica Presentation in BPS

In this study, two variables were found to be significantly associated with positivity to S. enterica in BPS: exchanging embryonated eggs and the presence of debeaked chickens, both increasing the risk of positivity to this pathogen. This situation could be explained because these two variables are a reflection of the potential relationship between BPS and commercial flocks [80]. Debeaking is a common and recommended practice in intensive systems to avoid behaviors such as cannibalism and pecking during the production process [81], consequently it is a common practice in large-scale production systems. The presence of debeaked chickens could provide evidence of the existence of animal movement, with the possible acquisition of replacement animals from commercial flocks in BPS and the selling of embryonated eggs, contributing to the dissemination of pathogens and their circulation [34]. It should be mentioned that the prevalence of S. enterica and STEC in animals from commercial production systems is usually higher than in BPS [48,82,83,84]. As additional background, both factors were found at a low frequency within the sample, which could indicate that relationships between commercial production systems and BPS are infrequent.
The Gini–Simpson index of species diversity was shown to be a statistically significant variable in both the STEC and the Enterobacteria models. This indicates that, since a BPS has more species (giving an index value closer to 1), there is a greater risk of these systems showing positivity to STEC or to both studied agents at the same time. The existence of more than one species on a farm could be an indicator of a high rate of contact between different animals, promoting the appearance of emerging diseases and their spread to other BPS [68]. Additionally, for the STEC model, the presence of nearby commercial production systems also acted as a variable that increased the risk of positivity. This could be related to the aforementioned points regarding the contact of BPS with commercial plants, considering that the latter tend to present higher prevalence values for several pathogens of importance to public and animal health [48,82,83,84]. These results highlight the importance of surveillance in this neglected animal and human population, as evidence suggests that direct contact with farm animals could act as a predictor of progression to HUS in human cases of STEC infection [85,86].
Finally, the type of confinement in which the animals are kept was determined as a factor that decreased the risk of positivity to both of the enterobacteria under study. Animals kept in a mixed confinement system might have a lower rate of contact with other animals and people, thus decreasing the probability of pathogen dissemination and decreasing the expected prevalence value for the studied agents [87]. Another variable that could influence animal contact is the existence of functional fences. Despite its importance as a reported risk factor in other studies [88,89], only six of the fourteen Enterobacteria-positive BPS maintained functional fences capable of retaining animals and preventing contact between them.
Being a user of INDAP or PRODESAL was determined as a factor that increased the risk of positivity to both agents. For the particular case of this study, those BPS owners who were users of INDAP/PRODESAL raised a greater number of animals, had a greater diversity of species and maintained several reservoir species that are included in various passive surveillance programs carried out by official entities belonging to SAG [70,90]. In addition, BPS owners who are users of INDAP or PRODESAL may belong to programs in the areas of forestry, agriculture or livestock, and therefore they do not necessarily receive veterinary advice for their animals. Furthermore, most BPS owners that were users of these entities only received economic aid. According to the above, it may be expected that INDAP/PRODESAL use represents a factor that increases the risk of positivity to Enterobacteriaceae.

5. Conclusions

The biosecurity measures observed empirically in BPS were factors that are not adopted as routine practices in the management of these systems. This fact could be explained by the infrequent adoption or updating of measures aimed at the handling of animals and biosecurity conditions, or by the fact that efforts were directed to other primary or secondary economic activities that provided an income to the family. Another relevant factor was the low frequency of relationships between BPS owners and professionals in the area of animal health, due to multiple causes, which generates little epidemiological surveillance of some zoonotic pathogens and their consequent under-notification.
This study represents an actualization of the epidemiological situation with regard to S. enterica, observing a similar behavior to that previously described for this region, and it is the first report on the circulation of STEC in these neglected animal populations. Since these bacteria are zoonotic pathogens that could have a great impact on public health, the results of this study highlight the need to generate integrated surveillance programs for BPS. These programs could be based on risk, categorizing existing risk factors to establish which of them need immediate attention. In addition, in order to be successful, surveillance programs should be carried out in conjunction with the people for whom the actions to be implemented are intended, under a One Health approach. It is necessary for the generated relationship between official health entities and BPS owners to be based on trust, in order to obtain an effective and reliable two-way line of communication. In this way, by integrating scientific tools with the empirical learning that the owners possess, it will also be possible to better understand the social and cultural context that surrounds these systems, which is an essential task for the generation of effective preventive measures to stop possible future outbreaks in high-risk populations.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ijerph182010730/s1, applied questionnaire.

Author Contributions

Conceptualization, R.A.-M., E.P.-M., B.F.-S. and C.U.-E.; methodology, R.A.-M., E.P.-M., B.F.-S. and C.U.-E.; software, R.A.-M., E.P.-M., B.F.-S. and C.U.-E.; validation, R.A.-M., E.P.-M., B.F.-S., C.U.-E. and N.G.; formal analysis, R.A.-M., E.P.-M., B.F.-S., C.U.-E. and N.G.; investigation, R.A.-M., E.P.-M. and N.G.; resources, R.A.-M.; data curation, R.A.-M. and E.P.-M.; writing—original draft preparation, R.A.-M., E.P.-M., B.F.-S. and C.U.-E.; writing—review and editing, R.A.-M. and N.G.; visualization, R.A.-M., E.P.-M., B.F.-S., C.U.-E. and N.G.; supervision, R.A.-M.; project administration, R.A.-M.; funding acquisition, R.A.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT), grant number 11180476.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, in accordance with the biosafety standards of a level 2 laboratory, according to the CONICYT Manual of Biosecurity standards (https://www.conicyt.cl/fondecyt/files/2018/08/MANUAL-DE-NORMAS-DE-BIOSEGURIDAD.pdf accessed on 15 November 2019), and approved by the Biosecurity Institutional Review Board (FAVET-UCH permit code 131) and bioethics committee of CICUA-UChile (permit code 18205-VET-UCH on 27 November 2018).

Informed Consent Statement

Informed consent was obtained from all BPS owners involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available because they are part of an ongoing project not yet published.

Acknowledgments

The authors acknowledge all members of the Zoonotic Agents Epidemiology Group and members of the Microbiology Laboratory from Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, for their support in sample and data collection and sample processing. The authors also want to thank Hannah Temple for proof-reading the manuscript and for English language editing.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Conan, A.; Goutard, F.L.; Sorn, S.; Vong, S. Biosecurity measures for backyard poultry in developing countries: A systematic review. BMC Vet. Res. 2012, 8, 240. [Google Scholar] [CrossRef]
  2. FAO. Integrated Backyard Systems; FAO Animal Production and Health Paper; FAO: Rome, Italy, 2000. [Google Scholar]
  3. FAO. Biosecurity for Highly Pathogenic Avian Influenza; FAO Animal Production and Health Paper; FAO: Rome, Italy, 2008; p. 81. [Google Scholar]
  4. Manning, J.; Gole, V.; Chousalkar, K. Screening for Salmonella in backyard chickens. Prev. Vet. Med. 2015, 120, 241–245. [Google Scholar] [CrossRef]
  5. Behravesh, C.B.; Brinson, D.; Hopkins, B.A.; Gomez, T.M. Backyard Poultry Flocks and Salmonellosis: A Recurring, Yet Preventable Public Health Challenge. Clin. Infect. Dis. 2014, 58, 1432–1438. [Google Scholar] [CrossRef]
  6. Alegria-Moran, R.; Rivera, D.; Toledo, V.; Moreno-Switt, A.I.; Hamilton-West, C. First detection and characterization of Salmonella spp. in poultry and swine raised in backyard production systems in central Chile. Epidemiol. Infect. 2017, 145, 3180–3190. [Google Scholar] [CrossRef]
  7. Di Pillo, F.; Anríquez, G.; Alarcón, P.; Jimenez-Bluhm, P.; Galdames, P.; Nieto, V.; Schultz-Cherry, S.; Hamilton-West, C. Backyard poultry production in Chile: Animal health management and contribution to food access in an upper middle-income country. Prev. Vet. Med. 2019, 164, 41–48. [Google Scholar] [CrossRef]
  8. Kumar, M.; Dahiya, S.P.; Ratwan, P. Backyard poultry farming in India: A tool for nutritional security and women empowerment. Biol. Rhythm Res. 2021, 52, 1476–1491. [Google Scholar] [CrossRef]
  9. Mottet, A.; Tempio, G. Global poultry production: Current state and future outlook and challenges. World’s Poult. Sci. J. 2017, 73, 245–256. [Google Scholar] [CrossRef]
  10. Correia-Gomes, C.; Sparks, N. Exploring the attitudes of backyard poultry keepers to health and biosecurity. Prev. Vet. Med. 2020, 174, 104812. [Google Scholar] [CrossRef]
  11. Bravo-Vasquez, N.; Di Pillo, F.; Lazo, A.; Jiménez-Bluhm, P.; Schultz-Cherry, S.; Hamilton-West, C. Presence of influenza viruses in backyard poultry and swine in El Yali wetland, Chile. Prev. Vet. Med. 2016, 134, 211–215. [Google Scholar] [CrossRef] [PubMed]
  12. Neira, V.; Brito, B.; Mena, J.; Culhane, M.; Apel, M.I.; Max, V.; Perez, P.; Moreno, V.; Mathieu, C.; Johow, M.; et al. Epidemiological investigations of the introduction of porcine reproductive and respiratory syndrome virus in Chile, 2013–2015. PLoS ONE 2017, 12, e0181569. [Google Scholar] [CrossRef] [PubMed]
  13. Bravo-Vasquez, N.; Baumberger, C.; Jimenez-Bluhm, P.; Di Pillo, F.; Lazo, A.; Sanhueza, J.; Schultz-Cherry, S.; Hamilton-West, C. Risk factors and spatial relative risk assessment for influenza A virus in poultry and swine in backyard production systems of central Chile. Vet. Med. Sci. 2020, 6, 518–526. [Google Scholar] [CrossRef] [PubMed]
  14. Hanlon, K.E.; Miller, M.F.; Guillen, L.M.; Echeverry, A.; Dormedy, E.; Cemo, B.; Branham, L.A.; Sanders, S.; Brashears, M.M. Presence of Salmonella and Escherichia coli O157 on the hide, and presence of Salmonella, Escherichia coli O157 and Campylobacter in feces from small-ruminant (goat and lamb) samples collected in the United States, Bahamas and Mexico. Meat Sci. 2018, 135, 1–5. [Google Scholar] [CrossRef]
  15. Pires, S.M.; Desta, B.N.; Mughini-Gras, L.; Mmbaga, B.T.; Fayemi, O.E.; Salvador, E.M.; Gobena, T.; Majowicz, S.E.; Hald, T.; Hoejskov, P.S.; et al. Burden of foodborne diseases: Think global, act local. Curr. Opin. Food Sci. 2021, 39, 152–159. [Google Scholar] [CrossRef] [PubMed]
  16. Lee, H.; Yoon, Y. Etiological Agents Implicated in Foodborne Illness World Wide. Food Sci. Anim. Resour. 2021, 41, 1–7. [Google Scholar] [CrossRef] [PubMed]
  17. Abebe, E.; Gugsa, G.; Ahmed, M. Review on Major Food-Borne Zoonotic Bacterial Pathogens. J. Trop. Med. 2020, 2020, 4674235. [Google Scholar] [CrossRef] [PubMed]
  18. Majowicz, S.E.; Scallan, E.; Jones-Bitton, A.; Sargeant, J.M.; Stapleton, J.; Angulo, F.J.; Yeung, D.H.; Kirk, M.D. Global Incidence of Human Shiga Toxin–Producing Escherichia coli Infections and Deaths: A Systematic Review and Knowledge Synthesis. Foodborne Pathog. Dis. 2014, 11, 447–455. [Google Scholar] [CrossRef]
  19. Stanaway, J.D.; Parisi, A.; Sarkar, K.; Blacker, B.F.; Reiner, R.C.; Hay, S.I.; Nixon, M.R.; Dolecek, C.; James, S.L.; Mokdad, A.H.; et al. The global burden of non-typhoidal salmonella invasive disease: A systematic analysis for the Global Burden of Disease Study 2017. Lancet Infect. Dis. 2019, 19, 1312–1324. [Google Scholar] [CrossRef]
  20. Allocati, N.; Masulli, M.; Alexeyev, M.F.; Di Ilio, C. Escherichia coli in Europe: An Overview. Int. J. Environ. Res. Public Health 2013, 10, 6235–6254. [Google Scholar] [CrossRef]
  21. Croxen, M.A.; Law, R.J.; Scholz, R.; Keeney, K.M.; Wlodarska, M.; Finlay, B.B. Recent Advances in Understanding Enteric Pathogenic Escherichia coli. Clin. Microbiol. Rev. 2013, 26, 822–880. [Google Scholar] [CrossRef]
  22. Clements, A.; Young, J.C.; Constantinou, N.; Frankel, G. Infection strategies of enteric pathogenic Escherichia coli. Gut Microbes 2012, 3, 71–87. [Google Scholar] [CrossRef]
  23. Osman, K.M.; Hessain, A.M.; Abo-shama, U.H.; Girh, Z.M.; Kabli, S.A.; Hemeg, H.A.; Moussa, I.M. An alternative approach for evaluating the phenotypic virulence factors of pathogenic Escherichia coli. Saudi J. Biol. Sci. 2018, 25, 195–197. [Google Scholar] [CrossRef]
  24. Baranzoni, G.M.; Fratamico, P.M.; Gangiredla, J.; Patel, I.; Bagi, L.K.; Delannoy, S.; Fach, P.; Boccia, F.; Anastasio, A.; Pepe, T. Characterization of Shiga Toxin Subtypes and Virulence Genes in Porcine Shiga Toxin-Producing Escherichia coli. Front. Microbiol. 2016, 7, 574. [Google Scholar] [CrossRef]
  25. Issenhuth-Jeanjean, S.; Roggentin, P.; Mikoleit, M.; Guibourdenche, M.; de Pinna, E.; Nair, S.; Fields, P.I.; Weill, F.-X. Supplement 2008–2010 (no. 48) to the White–Kauffmann–Le Minor scheme. Res. Microbiol. 2014, 165, 526–530. [Google Scholar] [CrossRef]
  26. Rivas, M.; Chinen, I.; Miliwebsky, E.; Masana, M. Risk Factors for Shiga Toxin-Producing Escherichia coli-Associated Human Diseases. Microbiol. Spectr. 2014, 2. [Google Scholar] [CrossRef]
  27. Crump John, A.; Sjölund-Karlsson, M.; Gordon Melita, A.; Parry Christopher, M. Epidemiology, Clinical Presentation, Laboratory Diagnosis, Antimicrobial Resistance, and Antimicrobial Management of Invasive Salmonella Infections. Clin. Microbiol. Rev. 2015, 28, 901–937. [Google Scholar] [CrossRef] [PubMed]
  28. Graziani, C.; Losasso, C.; Luzzi, I.; Ricci, A.; Scavia, G.; Pasquali, P. Chapter 5—Salmonella. In Foodborne Diseases, 3rd ed.; Dodd, C.E.R., Aldsworth, T., Stein, R.A., Cliver, D.O., Riemann, H.P., Eds.; Academic Press: Cambridge, MA, USA, 2017; pp. 133–169. [Google Scholar] [CrossRef]
  29. Bryan, A.; Youngster, I.; McAdam, A.J. Shiga Toxin Producing Escherichia coli. Clin. Lab. Med. 2015, 35, 247–272. [Google Scholar] [CrossRef] [PubMed]
  30. Eng, S.-K.; Pusparajah, P.; Ab Mutalib, N.-S.; Ser, H.-L.; Chan, K.-G.; Lee, L.-H. Salmonella: A review on pathogenesis, epidemiology and antibiotic resistance. Front. Life Sci. 2015, 8, 284–293. [Google Scholar] [CrossRef]
  31. Gyles, C.L. Shiga toxin-producing Escherichia coli: An overview1. J. Anim. Sci. 2007, 85, E45–E62. [Google Scholar] [CrossRef]
  32. Marier, E.A.; Snow, L.C.; Floyd, T.; McLaren, I.M.; Bianchini, J.; Cook, A.J.C.; Davies, R.H. Abattoir based survey of Salmonella in finishing pigs in the United Kingdom 2006–2007. Prev. Vet. Med. 2014, 117, 542–553. [Google Scholar] [CrossRef]
  33. Andino, A.; Hanning, I. Salmonella enterica: Survival, Colonization, and Virulence Differences among Serovars. Sci. World J. 2015, 2015, 520179. [Google Scholar] [CrossRef] [PubMed]
  34. Barreto, M.; Castillo-Ruiz, M.; Retamal, P. Salmonella enterica: Una revisión de la trilogía agente, hospedero y ambiente, y su trascendencia en Chile. Rev. Chil. Infectol. 2016, 33, 547–557. [Google Scholar] [CrossRef] [PubMed]
  35. Martínez, M.C.; Retamal, P.; Rojas-Aedo, J.F.; Fernández, J.; Fernández, A.; Lapierre, L. Multidrug-Resistant Outbreak-Associated Salmonella Strains in Irrigation Water from the Metropolitan Region, Chile. Zoonoses Public Health 2017, 64, 299–304. [Google Scholar] [CrossRef] [PubMed]
  36. Persad Anil, K.; LeJeune Jefrey, T. Animal Reservoirs of Shiga Toxin-Producing Escherichia coli. Microbiol. Spectr. 2014, 2. [Google Scholar] [CrossRef]
  37. Frozi, J.B.; Domingues, J.R.; Ramires, L.M.; Corrêa da Rosa, J.M.; Sant’Anna da Costa Silva, A.L.; Martins, A.G. Survival of Shiga toxin-producing Escherichia coli O157:H7 in Minas frescal cheese. Food Sci. Technol. 2015, 35, 108–114. [Google Scholar] [CrossRef]
  38. Ferreira, M.R.A.; Silva, T.d.S.; Stella, A.E.; Conceição, F.R.; Reis, E.F.d.; Moreira, C.N. Detection of virulence factors and antimicrobial resistance patterns in shiga toxin-producing Escherichia coli isolates from sheep. Pesqui. Veterinária Bras. 2015, 35, 775–780. [Google Scholar] [CrossRef]
  39. Sanches, L.A.; da Silva Gomes, M.; Teixeira, R.H.F.; Cunha, M.P.V.; de Oliveira, M.G.X.; Vieira, M.A.M.; Gomes, T.A.T.; Knobl, T. Captive wild birds as reservoirs of enteropathogenic E. coli (EPEC) and Shiga-toxin producing E. coli (STEC). Braz. J. Microbiol. 2017, 48, 760–763. [Google Scholar] [CrossRef] [PubMed]
  40. Luis, L.E.; Maturrano, L.; Hermelinda, R.G.; Víctor, Z.H.; Rosadio, R. Genotipificación, evaluación toxigénica in vitro y sensibilidad a antibióticos de cepas de Escherichia coli aisladas de casos diarreicos y fatales en alpacas neonatas. Braz. J. Microbiol. 2012, 23, 9. [Google Scholar] [CrossRef]
  41. Blanco Crivelli, X.; Bonino, M.P.; Von Wernich Castillo, P.; Navarro, A.; Degregorio, O.; Bentancor, A. Detection and Characterization of Enteropathogenic and Shiga Toxin-Producing Escherichia coli Strains in Rattus spp. from Buenos Aires. Front. Microbiol. 2018, 9, 199. [Google Scholar] [CrossRef] [PubMed]
  42. Leotta, G.; Suzuki, K.; Alvarez, F.; Nunez, L.; Silva, M.; Castro, L.; Faccioli, M.; Zarate, N.; Weiler, N.; Alvarez, M. Prevalence of Salmonella spp. in backyard chickens in Paraguay. Int. J. Poult. Sci. 2010, 9, 533–536. [Google Scholar] [CrossRef]
  43. Xavier, J.; Pascal, D.; Crespo, E.; Schell, H.L.; Trinidad, J.A.; Bueno, D.J. Seroprevalence of Salmonella and Mycoplasma infection in backyard chickens in the state of Entre Ríos in Argentina. Poult. Sci. 2011, 90, 746–751. [Google Scholar] [CrossRef]
  44. Alves Ferreira, M.R.; Freitas Filho, E.G.; Naves Pinto, J.F.; Dias, M.; Nunes Moreira, C.N. Isolation, prevalence, and risk factors for infection by shiga toxin-producing Escherichia coli (STEC) in dairy cattle. Trop. Anim. Health Prod. 2014, 46, 635–639. [Google Scholar] [CrossRef]
  45. Vidal, M.; Escobar, P.; Prado, V.; Hormazabal, J.C.; Vidal, R. Distribution of putative adhesins in Shiga toxin-producing Escherichia coli (STEC) strains isolated from different sources in Chile. Epidemiol. Infect. 2007, 135, 688–694. [Google Scholar] [CrossRef] [PubMed]
  46. Gómez Vega, E. Identificación de Cepas de Salmonella spp. Resistentes a Antimicrobianos, y Factores de Riesgo para su Circulación, en Aves y Cerdos Mantenidos En Sistemas Productivos de Traspatio de la Región del Libertador General Bernardo O’Higgings, Chile. Master’s Thesis, Magister en Ciencias Animales y Veterinarias, Mención Medicina Preventiva Animal, Universidad de Chile, Santiago, Chile, 2014. [Google Scholar]
  47. Salas Soto, R.A. Salmonella spp, Resistencia a Antimicrobianos y Caracterización de Medidas de Bioseguridad en Sistemas Productivos de Traspatio Vecinos a La Reserva Nacional El Yali. Master’s Thesis, Magister en Ciencias Animales y Veterinarias, Mención Medicina Preventiva Animal, Universidad de Chile, Santiago, Chile, 2016. [Google Scholar]
  48. Galarce, N.; Sánchez, F.; Fuenzalida, V.; Ramos, R.; Escobar, B.; Lapierre, L.; Paredes-Osses, E.; Arriagada, G.; Alegría-Morán, R.; Lincopán, N.; et al. Phenotypic and Genotypic Antimicrobial Resistance in Non-O157 Shiga Toxin-Producing Escherichia coli Isolated from Cattle and Swine in Chile. Front. Vet. Sci. 2020, 7, 367. [Google Scholar] [CrossRef] [PubMed]
  49. Dohoo, R.; Martin, W.; Stryhn, H. Methods in Epidemiologic Research, 1st ed.; VER Inc.: Charlottetown, PE, Canada, 2012; p. 890. [Google Scholar]
  50. I.N.E. National Agricultural Census; INE: Santiago, Chile, 2007. [Google Scholar]
  51. Cebula, T.A.; Payne, W.L.; Feng, P. Simultaneous identification of strains of Escherichia coli serotype O157:H7 and their Shiga-like toxin type by mismatch amplification mutation assay-multiplex PCR. J. Clin. Microbiol. 1995, 33, 248–250. [Google Scholar] [CrossRef] [PubMed]
  52. Malorny, B.; Hoorfar, J.; Bunge, C.; Helmuth, R. Multicenter Validation of the Analytical Accuracy of Salmonella PCR: Towards an International Standard. Appl. Environ. Microbiol. 2003, 69, 290–296. [Google Scholar] [CrossRef]
  53. Dohoo, R.; Martin, W.; Stryhn, H. Veterinary Epidemiologic Research, 2nd ed.; VER Inc.: Charlottetown, PE, Canada, 2009; p. 865. [Google Scholar]
  54. Hosmer, D.W.; Lemeshow, S.; Rodney, S. Applied Logistic Regression, 3rd ed.; Wiley: Hoboken, NJ, USA, 2013. [Google Scholar] [CrossRef]
  55. Mehta, C.R.; Patel, N.R. Exact logistic regression: Theory and examples. Stat. Med. 1995, 14, 2143–2160. [Google Scholar] [CrossRef] [PubMed]
  56. Hosmer, D.W.; Hosmer, T.; Le Cessie, S.; Lemeshow, S. A comparison of goodness-of-fit tests for the logistic regression model. Stat. Med. 1997, 16, 965–980. [Google Scholar] [CrossRef]
  57. Pavoine, S.; Ricotta, C. A simple translation from indices of species diversity to indices of phylogenetic diversity. Ecol. Indic. 2019, 101, 552–561. [Google Scholar] [CrossRef]
  58. R_Core_Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021. [Google Scholar]
  59. RStudio_Team. RStudio: Integrated Development for R; RStudio, PBC: Boston, MA, USA, 2021. [Google Scholar]
  60. Pires, A.F.A.; Peterson, A.; Baron, J.N.; Adams, R.; Martínez-López, B.; Moore, D. Small-scale and backyard livestock owners needs assessment in the western United States. PLoS ONE 2019, 14, e0212372. [Google Scholar] [CrossRef]
  61. Habiyaremye, N.; Ochieng, J.; Heckelei, T. Economic analysis of integrated vegetable–poultry production systems in the Babati District of Tanzania. Agric. Food Secur. 2021, 10, 1. [Google Scholar] [CrossRef]
  62. Pollock, S.L.; Stephen, C.; Skuridina, N.; Kosatsky, T. Raising Chickens in City Backyards: The Public Health Role. J. Community Health 2012, 37, 734–742. [Google Scholar] [CrossRef]
  63. Espinosa, R.; Tago, D.; Treich, N. Infectious Diseases and Meat Production. Environ. Resour. Econ. 2020, 76, 1019–1044. [Google Scholar] [CrossRef] [PubMed]
  64. Jimenez-Bluhm, P.; Di Pillo, F.; Bahl, J.; Osorio, J.; Schultz-Cherry, S.; Hamilton-West, C. Circulation of influenza in backyard productive systems in central Chile and evidence of spillover from wild birds. Prev. Vet. Med. 2018, 153, 1–6. [Google Scholar] [CrossRef]
  65. Mariner, J.C.; Jones, B.A.; Hendrickx, S.; El Masry, I.; Jobre, Y.; Jost, C.C. Experiences in Participatory Surveillance and Community-based Reporting Systems for H5N1 Highly Pathogenic Avian Influenza: A Case Study Approach. EcoHealth 2014, 11, 22–35. [Google Scholar] [CrossRef]
  66. Ameri, A.A.; Hendrickx, S.; Jones, B.; Mariner, J.; Mehta, P.; Pissang, C. Introduction to Participatory Epidemiology and Its Application to Highly Pathogenic Avian Influenza Participatory Disease Surveillance: A Manual for Participatory Disease Surveillance Practitioners; ILRI: Nairobu, Kenya, 2009. [Google Scholar]
  67. Di Pillo, F.; Jimenez-Bluhm, P.; Baumberger, C.; Marambio, V.; Galdames, P.; Monti, G.; Schultz-Cherry, S.; Hamilton-West, C. Movement Restriction and Increased Surveillance as Efficient Measures to Control the Spread of Highly Pathogenic Avian Influenza in Backyard Productive Systems in Central Chile. Front. Vet. Sci. 2020, 7, 424. [Google Scholar] [CrossRef]
  68. Schembri, N.; Hernandez-Jover, M.; Toribio, J.A.L.M.L.; Holyoake, P.K. On-farm characteristics and biosecurity protocols for small-scale swine producers in eastern Australia. Prev. Vet. Med. 2015, 118, 104–116. [Google Scholar] [CrossRef]
  69. McDonagh, A.; Leibler, J.H.; Mukherjee, J.; Thachil, A.; Goodman, L.B.; Riekofski, C.; Nee, A.; Smyth, K.; Forrester, J.; Rosenbaum, M.H. Frequent human-poultry interactions and low prevalence of Salmonella in backyard chicken flocks in Massachusetts. Zoonoses Public Health 2019, 66, 92–100. [Google Scholar] [CrossRef]
  70. Servicio Agrícola y Ganadero (SAG); Instituto Interamericano de Cooperación para la Agricultura (IICA); Ministerio de Agricultura (Chile). Perfil: Proyecto Nacional de Prevención de Influenza Aviar. Available online: https://repositorio.iica.int/handle/11324/9108 (accessed on 25 October 2018).
  71. Cornejo, J.; Pokrant, E.; Figueroa, F.; Riquelme, R.; Galdames, P.; Di Pillo, F.; Jimenez-Bluhm, P.; Hamilton-West, C. Assessing Antibiotic Residues in Poultry Eggs from Backyard Production Systems in Chile, First Approach to a Non-Addressed Issue in Farm Animals. Animals 2020, 10, 1056. [Google Scholar] [CrossRef] [PubMed]
  72. Suarez, D.L.; Pantin-Jackwood, M.J. Recombinant viral-vectored vaccines for the control of avian influenza in poultry. Vet. Microbiol. 2017, 206, 144–151. [Google Scholar] [CrossRef] [PubMed]
  73. Bitsouni, V.; Lycett, S.; Opriessnig, T.; Doeschl-Wilson, A. Predicting vaccine effectiveness in livestock populations: A theoretical framework applied to PRRS virus infections in pigs. PLoS ONE 2019, 14, e0220738. [Google Scholar] [CrossRef] [PubMed]
  74. Nair, D.; Venkitanarayanan, K.; Kollanoor Johny, A. Antibiotic-Resistant Salmonella in the Food Supply and the Potential Role of Antibiotic Alternatives for Control. Foods 2018, 7, 167. [Google Scholar] [CrossRef]
  75. Pavez-Muñoz, E.; González, C.; Fernández-Sanhueza, B.; Sánchez, F.; Escobar, B.; Ramos, R.; Fuenzalida, V.; Galarce, N.; Arriagada, G.; Neira, V.; et al. Antimicrobial Usage Factors and Resistance Profiles of Shiga Toxin-Producing Escherichia coli in Backyard Production Systems From Central Chile. Front. Vet. Sci. 2021, 7. [Google Scholar] [CrossRef]
  76. Rivera, D.; Allel, K.; Dueñas, F.; Tardone, R.; Soza, P.; Hamilton-West, C.; Moreno-Switt, A.I. Screening the Presence of Non-Typhoidal Salmonella in Different Animal Systems and the Assessment of Antimicrobial Resistance. Animals 2021, 11, 1532. [Google Scholar] [CrossRef]
  77. Sánchez, F.; Fuenzalida, V.; Ramos, R.; Escobar, B.; Neira, V.; Borie, C.; Lapierre, L.; López, P.; Venegas, L.; Dettleff, P.; et al. Genomic features and antimicrobial resistance patterns of Shiga toxin-producing Escherichia coli strains isolated from food in Chile. Zoonoses Public Health 2021, 68, 226–238. [Google Scholar] [CrossRef]
  78. Shah, D.H.; Board, M.M.; Crespo, R.; Guard, J.; Paul, N.C.; Faux, C. The occurrence of Salmonella, extended-spectrum β-lactamase producing Escherichia coli and carbapenem resistant non-fermenting Gram-negative bacteria in a backyard poultry flock environment. Zoonoses Public Health 2020, 67, 742–753. [Google Scholar] [CrossRef] [PubMed]
  79. Varga, C.; Guerin, M.T.; Brash, M.L.; Slavic, D.; Boerlin, P.; Susta, L. Antimicrobial resistance in fecal Escherichia coli and Salmonella enterica isolates: A two-year prospective study of small poultry flocks in Ontario, Canada. BMC Vet. Res. 2019, 15, 464. [Google Scholar] [CrossRef]
  80. Derksen, T.; Lampron, R.; Hauck, R.; Pitesky, M.; Gallardo, R.A. Biosecurity Assessment and Seroprevalence of Respiratory Diseases in Backyard Poultry Flocks Located Close to and Far from Commercial Premises. Avian Dis. 2017, 62, 1–5. [Google Scholar] [CrossRef] [PubMed]
  81. Servicio Agrícola y Ganadero (SAG). Guía de Buenas Prácticas Sobre Bienestar Animal en los Diferentes Sistemas de Producción de Huevos. Available online: https://www.sag.gob.cl/sites/default/files/gbp-ba_produccion_huevos_oct-2018.pdf (accessed on 19 July 2020).
  82. Hussein, H.S.; Sakuma, T. Prevalence of Shiga Toxin-Producing Escherichia coli in Dairy Cattle and Their Products. J. Dairy Sci. 2005, 88, 450–465. [Google Scholar] [CrossRef]
  83. Mughini-Gras, L.; van Pelt, W.; van der Voort, M.; Heck, M.; Friesema, I.; Franz, E. Attribution of human infections with Shiga toxin-producing Escherichia coli (STEC) to livestock sources and identification of source-specific risk factors, The Netherlands (2010–2014). Zoonoses Public Health 2018, 65, e8–e22. [Google Scholar] [CrossRef]
  84. Gad, A.H.; Abo-Shama, U.H.; Harclerode, K.K.; Fakhr, M.K. Prevalence, Serotyping, Molecular Typing, and Antimicrobial Resistance of Salmonella Isolated From Conventional and Organic Retail Ground Poultry. Front. Microbiol. 2018, 9, 2653. [Google Scholar] [CrossRef]
  85. Vachon, M.S.; Khalid, M.; Tarr, G.A.M.; Hedberg, C.; Brown, J.A. Farm animal contact is associated with progression to Hemolytic uremic syndrome in patients with Shiga toxin-producing Escherichia coliIndiana, 2012–2018. One Health 2020, 11, 100175. [Google Scholar] [CrossRef]
  86. Mulder, A.C.; van de Kassteele, J.; Heederik, D.; Pijnacker, R.; Mughini-Gras, L.; Franz, E. Spatial Effects of Livestock Farming on Human Infections With Shiga Toxin-Producing Escherichia coli O157 in Small but Densely Populated Regions: The Case of the Netherlands. GeoHealth 2020, 4, e2020GH000276. [Google Scholar] [CrossRef] [PubMed]
  87. Reperant, L.A.; Cornaglia, G.; Osterhaus, A. The Importance of Understanding the Human–Animal Interface. In One Health: The Human-Animal-Environment Interfaces in Emerging Infectious Diseases: The Concept and Examples of a One Health Approach; Mackenzie, J.S., Jeggo, M., Daszak, P., Richt, J.A., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; pp. 49–81. [Google Scholar] [CrossRef]
  88. Díaz-Sánchez, S.; Sánchez, S.; Herrera-León, S.; Porrero, C.; Blanco, J.; Dahbi, G.; Blanco, J.E.; Mora, A.; Mateo, R.; Hanning, I.; et al. Prevalence of Shiga toxin-producing Escherichia coli, Salmonella spp. and Campylobacter spp. in large game animals intended for consumption: Relationship with management practices and livestock influence. Vet. Microbiol. 2013, 163, 274–281. [Google Scholar] [CrossRef] [PubMed]
  89. Ethèves, M.A.; Choisis, N.; Alvarez, S.; Dalleau, F.; Hascoat, J.; Gallard, V.; Cardinale, E. Risk factors for Salmonella enterica subsp. enterica persistence in broiler-chicken flocks on Reunion Island. Heliyon 2021, 7, e06278. [Google Scholar] [CrossRef] [PubMed]
  90. Servicio Agrícola y Ganadero (SAG). Actualización del Plan oficial de Control y Erradicación del Síndrome Respiratorio y Reproductivo Porcino en Chile. 2014. Available online: https://www.sag.gob.cl/sites/default/files/informe__prrs_a_semana_30-31-2014.pdf (accessed on 19 July 2020).
Table 1. Sample size determination by province, Metropolitana region.
Table 1. Sample size determination by province, Metropolitana region.
ProvinceN° BPS with BirdsN° BPS with SwineSample Size
Melipilla191020230
Chacabuco4267811
Santiago244619
Cordillera237294
Talagante387365
Maipo6329213
Total 73
Table 2. Descriptive characterization of species kept in BPS in the Metropolitana region.
Table 2. Descriptive characterization of species kept in BPS in the Metropolitana region.
SpeciesN° BPS%AM 1 Animals/BPSMin.Max.SD+ 2
Birds4047.06%59.987524123.51
Chickens only4350.59%57.5131000120.92
Pigs1821.17%6.301223.65
Horses2529.40%3.561102.17
Sheep1112.90%8.271304.49
Goats55.80%2.50140.72
Cows1315.20%11.611406.62
Rabbits44.70%7.501153.30
Dogs6070.60%3.921203.74
Cats4350.60%2.58181.90
Birds4047.06%59.987524123.51
1 Arithmetic mean. 2 Standard deviation.
Table 3. Handling, health and biosecurity characterization of BPS from Metropolitana region.
Table 3. Handling, health and biosecurity characterization of BPS from Metropolitana region.
ParameternAF 1 YesRF+ 2 YesAF 1 NoRF+ 2 No
General handling characteristics
Consumption and/or sale of animal products85800.9450.06
Animal breeding at least 20 years85510.60290.34
Animal handling by one person85590.69220.26
Animals kept in mixed confinement (free-range during the day and confined during night)85580.68270.32
Seasonal variation in the number of animals kept85350.41500.59
Produce their own replacement animals85620.73210.25
Handle sick animals85420.49420.49
Receive veterinary assistance or diagnosis85150.18700.82
Exchanging embryonated eggs8530.04800.94
Debeaked chickens8530.04790.93
Biosecurity characteristics
Contact between visitors and BPS animals85640.75200.24
Animals had access to a non-potable water source85400.47450.53
Appropriate dead animal disposal85400.47440.52
BPS neighbors (surrounded by other BPS)85420.49400.47
Contact with wildlife animals85690.81150.18
Contact with neighboring BPS85440.52400.47
Contact between chickens and pigs850.6330.37
Functional fences85480.56370.44
Footbath8500851
Pre-entry disinfection8500851
Post-stay disinfection8500851
Watercourse inside the BPS85520.61330.39
Nearby wetlands8510.01840.99
Neighbors with birds/pigs85420.49430.51
Proximity to intensive poultry/swine production8590.11760.89
Contact between BPS animals85610.72240.28
Indoor pets8530.04820.96
Contact between poultry and neighboring pets85370.44480.56
Pet access to animal waste85770.9180.09
Contact between BPS animals and sick people85790.9360.07
Relationship with government agricultural entities
Official veterinary service visits85160.19690.81
Official veterinary service sampling85110.13740.87
Official veterinary service results information8530.04820.96
Official veterinary service returns to BPS8550.06800.94
INDAP/PRODESAL users85140.16710.84
1 Absolute frequency. 2 Relative frequency.
Table 4. Number of positive BPS with respect to STEC and S. enterica by province.
Table 4. Number of positive BPS with respect to STEC and S. enterica by province.
ProvinceN° BPSN° BPS + S. entericaProvince PrevalenceN° BPS + STECProvince Prevalence
Melipilla3425.99%411.76%
Talagante7----
Cordillera5120%360%
Maipo1616.25%16.25%
Chacabuco13--215.38%
Santiago10----
Regional total8544.71%1011.76%
Table 5. Positive samples for STEC and S. enterica by province, BPS, animal species and Stx type.
Table 5. Positive samples for STEC and S. enterica by province, BPS, animal species and Stx type.
PathogenProvinceCodeAnimal SpecieStx1Stx2
STECMelipillaME001Cattle11
Cattle11
Cattle11
Swine11
ME010Duck01
Duck01
Duck10
ME011Hens10
Sheep10
ME024Sheep10
CordilleraCORD001Cattle01
CORD003Sheep10
CORD004Sheep10
Goat10
MaipoMAI009Hens01
ChacabucoCHAC003Sheep10
Sheep10
Cattle10
CHAC010Goat10
Sheep10
S. entericaMelipillaME023Hens
Goose
ME033Hens
CordilleraCORD002Hens
MaipoMAI013Hens
Table 6. Final model results from the multivariable logistic regression analysis. The p-value, odds ratio (OR) and 95% confidence interval (C.I.) with lower and upper limits are reported for risk factors associated with positivity to STEC in BPS.
Table 6. Final model results from the multivariable logistic regression analysis. The p-value, odds ratio (OR) and 95% confidence interval (C.I.) with lower and upper limits are reported for risk factors associated with positivity to STEC in BPS.
VariableCategoriesp-ValueOR95% IC
LowerUpper
(Intercept) 0.0010.00800.141
Gini–Simpson index 0.0301.7171.0542.799
Functional fencesNoreference
Yes0.2720.1290.0035.006
Proximity to intensive poultry/swine productionNoreference
Yes0.01920.6451.648258.706
Official veterinary service returns to BPSNoreference
Yes0.09817.0870.59495.11
Contact between poultry and neighboring petsNoreference
Yes0.2194.410.41546.88
Interaction: Functional fences/Contact between poultry and neighboring pets 0.7470.530.01124.902
Table 7. Final model results from the multivariable logistic regression analysis. The p-value, odds ratio (OR) and 95% confidence interval (C.I.) with lower and upper limits are reported for risk factors associated with positivity to S. enterica in BPS.
Table 7. Final model results from the multivariable logistic regression analysis. The p-value, odds ratio (OR) and 95% confidence interval (C.I.) with lower and upper limits are reported for risk factors associated with positivity to S. enterica in BPS.
VariableCategoriesp-ValueOR95% IC
LowerUpper
(Intercept) 0.0010.0130.0020.092
Exchanges embryonated eggsNoreference
Yes0.021391.745871.724
Presence of debeaked chickensNoreference
Yes0.0011566.9793486.896
Table 8. Final model results from the multivariable logistic regression analysis. The p-value, odds ratio (OR) and 95% confidence interval (C.I.) with lower and upper limits are reported for risk factors associated with positivity to STEC and S. enterica in BPS.
Table 8. Final model results from the multivariable logistic regression analysis. The p-value, odds ratio (OR) and 95% confidence interval (C.I.) with lower and upper limits are reported for risk factors associated with positivity to STEC and S. enterica in BPS.
VariableCategoriesp-ValueOR95% IC
LowerUpper
(Intercept) 0.0790.1720.0241.229
Gini–Simpson index 0.0301.5441.0442.284
Type of confinementFreereference
Mixed0.0020.0190.0012.796
Permanent0.4030.4660.0782.796
INDAP/PRODESAL usersNoreference
Yes0.02315.021.465154.082
None of the evaluated interactions showed statistical significance (p > 0.05).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Back to TopTop