This study intended to assess the application of farm biosecurity and husbandry practices in Colombia, a country experiencing a very rapid growth in the pig population. The study focused firstly on farms with inventories over 100 sows. In this cross-sectional study we used a combination of different methods for analysis of complex survey data to identify and characterize the structure and clustering patterns of Colombian swine farms. Also, we estimated how these patterns were associated with IAV detection at the population level. To our knowledge, this is the first study analyzing complex survey epidemiological data from swine farms in Colombia. This research approach allowed us to identify two clustering patterns of 176 farms based on the characteristics, biosecurity and husbandry practices reported by farmers. Also, to demonstrate epidemiological factors associated with influenza A virus detection. Thus, providing key baseline data to further investigate the risk factors for disease outbreaks in these farms and help to recognize gaps on biosecurity and failure on the current strategies for disease control and prevention in the Colombian pork industry. Our results also demonstrate the need of more awareness campaigns to reduce risky practices in the swine farms in the country.
For studies that analyze complex datasets of qualitative variables, the use of multivariable methods such as MCA is highly advised. During MCA, categorical data are transformed into cross tables and the results interpreted in a graphical manner from principal dimensions [6]. Thus, the first dimension explains as much variance as possible of the dataset, the second dimension is orthogonal to the first and displays as much of the remaining variance as possible, and so on. Based on these criteria, in our model, a two-dimension MCA solution was considered the most adequate. The first two dimensions represented 27.6% of total inertia; thus, the relationships between the data were reasonably (albeit not perfectly) suited for the MCA. Other studies in swine farms have reported comparable findings using MCA [38]. This tool helped us to identify which variables were the most important to obtain a structural farm characterization and to understand the diversity of characteristics among Colombian pig farms to provide a baseline assessment of their husbandry practices and biosecurity.
On the other hand, when combining multivariable methods such as MCA and HCA, the complex survey dataset was reduced to profiles that maximize farm inter-cluster variation and intra-cluster correlation, allowing us to identify clustering patterns of swine farms and providing key insights on relationships between data. In our study, these combined methods showed two clustering patterns of the 176 swine farms evaluated, based on their level of similarity and thereby illustrated the structure variability of these farms in the country. Additionally, these clusters were associated with higher odds of IAV detection, thus rising new research questions for future studies that help identify specific gaps on biosecurity that may represent each of the farm clusters. Multiple studies have explored risk factors of IAV infections in pig farms [39], however, few studies have focused on analysis of clustering patterns of complex datasets from epidemiological surveys and on establishing their associations with IAV detection at a farm level. In this sense, data provided from our multivariable analyses could be useful to more realistically understand which factors are affecting the transmission of infectious diseases among pig farms and highlight the need of campaigns among the swine farmers to reduce risky practices.
Pig farms evaluated in this study had in common several characteristics for both farm clusters observed. Among other common practices, we found the use of barriers before the entry of people, and restricted access for vehicles. However, about a third of the farms in cluster 2 did not have barriers preventing the entry of people, where most of these farms were farrow-to-finish small farms. These results agree with those by other studies which reported that larger farms located in high pig density regions implement higher BP [40]. In our study, most of the larger farms are likely to have a higher technical standard in biosecurity, but risky practices such as mixing of livestock species is still observed.
In our study, after multivariable analysis we observed a low (27.6%) percentage of the variance explained by the model, which suggests that the combination of BP and MP implemented by a given farm altogether with the farmer´s behaviors, have a certain degree of randomness and consequently, the farm clustering pattern still contain internal variability. Similar findings are reported in other studies conducted in pig farms in Argentina [41]. Also, other studies have documented that even the most important practice to the farmers and veterinarians, such as measures to reduce the risk of disease introduction by visitors and vehicles, are not applied on a considerable number of farms [40]. Therefore, to our understanding this is an indication of the complexity surrounding the evaluation of biosecurity, and also acknowledging that compliance of the measures is critical to the analysis of the situation. In our work, compliance was not assessed, and therefore the observed data heterogeneity is probably connected to the different degree of expertise of farmers or swine veterinarians, their personalities, their interpretation of biosecurity principles and their access to sources of technical information [40]. Recent studies have shown that compliance of BP varies depending on farmer´s behaviors, work experience and education [42]. In this context, the increase in the biosecurity standards by pig farmers could be also motivated by the presence of an outbreak of a new disease in the region [43], and that factor could further difficult the understanding of the complexity of the evaluation of pig farm biosecurity.
While there is a perception of good biosecurity implemented by swine farmers, our results showed that may be a failure in understanding of the biosecurity principles and compliance of protocols. High percentage of farms reported implementation of most measures assessed in the survey, however, among others risky practices such as raising other livestock within the same farm were still reported. Attitudes towards biosecurity can be also influenced by additional factors. Lack of credibility, trust and confusion to the specific recommendations that farmers should follow are some of these affecting factors [40]. In this sense, mixed livestock production is not advisable for several reasons. Potential for cross-species transmission of animal diseases that affect multiple species will be facilitated in areas where concentrations of different animal species co-exist [44]. Our results suggest that small-size pig farmers (< 300 sows) are more likely to engage in this risky practice of mixing livestock production systems. Different vertebrates may serve as potential reservoirs of pathogens within a farm, and for example, studies have shown that having other domestic animals on the same pig farm is associated with a greater prevalence of salmonellosis [45].
Regarding the farm access protocols applied to people and vehicles, it is important to highlight that about 20% of the farms did not carry out the registration of entering vehicles and people to the farm. It is known that farm entry records allows to identify and control the pathogens transmission in the event of outbreaks [40]. In overall the farms evaluated in our study reported similar BP to those described in other studies. However, use of physical barriers was not applied in 5.7% of these pig farms. Although small number of farms did not apply this measure, it is well known that physical barriers at entrance help to reduce the risk of disease spread in pig farming systems [46]. On more than half of the farms had down time of 48 hours or less for visitors before entering to the facilities. In addition, few farms (< 9%) did not implement a disinfection procedure for vehicles at the farm entry. Recent studies showed that the absence of BP during access of vehicles and people are risk factors for disease transmission [2]. People entering the swine farm (i.e., workers and visitors) have been identified as a major source for disease transmission during outbreaks of highly contagious diseases [47]. Transportation and other mechanical vectors such as fomites (boots and containers), and the movement of personnel has been also identified as risk factors to herd health and disease spread [48]. Thus, results from the present study, showed that education to the farm owners is still needed to improve the application of proper BP at the access to the farms, which clearly point to the need for veterinarians and other assistance professionals to continue raising farmers’ awareness of biosecurity topics.
Referring to other important aspects, 49.7% of the farms in this study were located close (within a 5 km) to other pig farms. Also, 64.8% of the farms in this study were located in an area of high density of pigs/km2. It has been reported that failure in compliance of farm biosecurity in high pig density and mixed farming areas may increase the risk of disease introduction or spread from or to other farms [38]. Although few farms (8–12%) acquired their gilts from animal fairs/markets or other farms, it has been reported an increased probability of disease transmission and spread in farms when purchasing animals from livestock saleyards [49]. Also, infected pigs moving from one farm to another increase the risk for disease spread [50]. Similarity, 16.1% of farms in this study reported that they brought pigs from multiple sources. Animals from different sources that are mixed in the same area, provide an excellent opportunity for disease spread [44]. In regards to some husbandry practices, a small proportion of farms (< 5%) did not implemented pest control programs and used farm-own made feed mix as a food source. But, the presence of rodents and the type of feed added to other factors such as poor hygiene, are generally considered to be important aspects associated with a high prevalence of bacterial diseases in pig farms [45].
Despite the limited sample size and selection criteria of farms included in this study, we believed the trends in the findings of this study may generally represent pig farming systems in Colombia. However, very limited assumptions can be made regarding the attitudes and behaviors for biosecurity practices compliance of pig farmers. Nevertheless, at the end of this study, a detailed report was provided to the national swine producer association. Therefore, we believe the provision of our findings, alongside other advisory recommendations will serve as an incentive to review and address the gaps found in biosecurity.
Using multivariable regression methods, we found that three variables were associated with higher odds of IAV detection: “location in an area with a high density of pigs in the region (V1)”, “farm size (V5)”, “facilities are allowed to dry after cleaning and disinfecting (V20)”. Several studies have demonstrated that farm size, distance between farms in areas with high densities of pigs, and density of pigs in a specific area are risk factors associated to several viral diseases [51]. In our study, we found that approximately half of the farms were located to less than 5 km from other farms and were located in a pork production area with a local animal density greater than 10 pigs/km2. In addition, we observed that some swine farms raised other livestock species making more complex the biosecurity scenario. Previous studies in the United Kingdom, Netherlands and Korea reported great level of difficulty in preventing the spread of viral diseases in dense livestock areas, mainly where a mixture of pigs with other animal species was present [52]. Likewise, a modeling study in China demonstrated how areas of high density of swine productions are highly associated with an increased risk of outbreaks of IAV [53]. Published data have showed that prevalence of IAV is highly associated with high densities of pigs, defined in terms of number of farms and number of pigs [54]. In our study, we found that the frequency of positive farms was significatively higher (p-value = 0.03) in geographical areas with higher densities of pigs. In this sense, pig density becomes a key element in biosecurity of the farms specially for new facilities. We also found that a positive IAV status increased significantly in larger farms (those with inventories of 300 or more sows), but information about association of farm size to IAV infection in piglets in production systems of Colombia is very limited. Several studies have identified that farm size is a risk factor for IAV infection in pigs in other locations of the world [55]. A metanalysis conducted in 2017, showed that high densities and number of pigs per farm are associated with higher IAV prevalence, suggesting that larger pig farms have higher odds of IAV detection or persistent infections [54]. Other studies revealed that large number of pigs as one influential risk factor for IAV seropositivity in sows and fattening pigs [56], and this factor may also strongly impact on the incidence of subclinical IAV infection [57].
We found an Influenza herd-level prevalence ranging from 2.1 to 39.6% (Median 14.6%) which is lower but similar to findings made for swine farms located in important pork producing regions in the world [19]. It also has been reported that IAV herd-level prevalence may vary over time showing seasonal patterns [19]. We also demonstrated that IAV is actively circulating in piglets and probably established across swine herds in the country. For IAV detection at the farm level we selected piglets between 3 and 12 weeks of age because they act as reservoir for enzootic infections in swine populations [58]. Moreover, piglets play a pivotal role in maintaining IAV endemicity in pig populations. Swine IAVs can endemically persist in farrow-to-finish farms, causing repeated disease outbreaks in pigs around 8 weeks of age [59]. Additionally, different factors contribute to IAV persistence within swine herds including population dynamics of farrow-to-finish farms, immune status of animals and the co-circulation of distinct subtypes [59]. Furthermore, the odds of IAV detection in farms were 7.35 times higher in cluster 2 compared to cluster 1, and cluster 2 was mainly represented by farms with only one production site, therefore these characteristics may also be contributing factors to the endemically persistency of the virus. Therefore, active surveillance should be routinely recommended to better understand the transmission within and between the swine production systems in Colombia.
The general need for routine monitoring of diseases in swine has pointed to the use of new sampling/testing methods which be adapted to the pig population structure and production practices of contemporary swine farms. In this sense, sample pooling strategies have been used for herd monitoring of influenza virus and other infectious diseases in swine an other animals [60]. This design based on sampling individual groups of animals provides flexibility in monitoring farms ranging widely in size and complexity. Furthermore, provides a powerful approach for detecting infection and similar methods have been widely applied for detecting Porcine Reproductive and Respiratory Syndrome Virus infection in one barn using as few as 2 OF samples. Under these same assumptions, if 2 oral fluid samples are collected from each of 3 barns on one farm, the probability of detection increases to 81% [61]. In our study, 8 OF samples were collected from 4 different groups of pigs within the same farm, and using the formula given by other authors [61] (assuming a within-herd IAV prevalence above 13%), the probability of IAV detection is greater than 40%. Because of the known high transmissibility of influenza viruses, it is expected that a large proportion of animals would face the exposure to the virus following IAV entry to a herd. A study conducted in vaccinated and non-vaccinated pigs showed that probability of detecting IAV in OF was 99% when within pen prevalence was higher than 18% [62]. In this sense, other studies reported that OF collected from pigs resulted in higher odd ratios of detecting a positive IAV sample by RT-PCR compared to individual pooled samples (nasal swabs), however, individual samples were most likely to yield a successful virus isolation [63]. Similarity, pooling specimens to increase efficiency of testing and cost effectiveness is another advantage obtained from the sample pooling strategies [64]. Studies performed using pools of 10 nasopharyngeal swine specimens containing either single or multiple positive specimens or specimens positive for other respiratory viruses showed no negative effect on IAV detection by RT-PCR [25]. In our study, testing of 5 pooled individual samples helped to increase our testing capacity and resource savings. A study reported that testing pools of 5 individual-samples did not affect the detection of IAV on oral swabs but increased testing capacity without sacrificing sensitivity [60]. The sensitivity of pooled samples may be lower than the sensitivity of individual samples and this practice may actually reduce the chances of detecting the virus since it may result into lowering the virus titer of the pooled sample, however, studies have shown that pen-based collection of OF is a sensitive method to detect influenza even when prevalence of the disease is low [62]. Thus, monitoring of IAV in swine farms using sample pooling strategies such as OF to detect virus infection at a herd level is a recommendable approach to follow under limited resource settings such as the swine productions in Colombia.
We demonstrated that IAV is widely circulating in Colombian swine populations at the main producing regions for pork industry. Therefore, the virus is not restricted to a particular geographic location, even if BP are in applied in the farms. In addition to the scarce information available regarding active surveillance and prevalence of IAV in the pork industry in Colombia, which could be interpreted as a little interest on detection of SI in the local industry, thus we hypothesize that conjunction of the lack of active detection of IAV in swine farms, no vaccine availability, and either deficient or not strict compliance of BP could be adding more difficulty on controlling IAV spread within and between pig farms in the country. Therefore, highlight the need for awareness campaigns to reduce risky practices and implementation of a vaccination program against SI (guarantying that vaccine strains match circulating viruses in swine) would be highly advised. Vaccination integrated with other BP is the most effective strategy to reduce IAV transmission and to control spread of SI, even when the disease is persistently endemic in piglets [58].
Controversially, in our study farms in which the facility to dry after cleaning and disinfection were associated with a higher odd of IAV detection. Given known facts, cleaning and disinfecting are critical parts of all biosecurity programs [65] since the goal of this process is to decrease pathogens load significantly to a point where disease transmission does not occur. However, many other factors may be affecting the persistency of the virus after cleaning and disinfection. For example, air and surfaces in swine barns during outbreaks of IAV can contain different viral loads representing different exposure levels for animals. A study showed that during SI outbreaks, detection of IAV from air was sustained up to 11 days from reported onset representing an exposure hazard to both swine and people [66]. Other experimental studies have demonstrated that IAV transmission is strongly modulated by temperature and humidity [67]. Therefore, it is possible that our finding is confounded by environmental factors such as persistence of airborne viral particles, local temperature, and humidity or that the period time to let dry the surfaces or the cleaning and disinfection process is not properly executed. Another aspect to highlight is the compliance of the measure surveyed. Even that majority of the farmers indicated that after washing and disinfecting they allowed to dry the facilities before use, based on the controversial finding, we may think that compliance to that measure was not completely applied. Social desirability is also a paradigm for measurement bias in surveys. Studies have showed that people can provide answer to surveys based on a normative behavior to appear correct to interviewers [68]. These and some other possible sources of bias could have influenced the results of our study, therefore, our findings should be taken with caution until they can be validated with further studies, in which environmental factors, compliance and other possible affecting variables are assessed.
Additionally, in our analysis using an independent vehicle to transport animals and feed was also found to increase the odds of IAV detection. Studies have revealed that feed transportation system and other mechanical vectors can be source of transmission of diseases in swine farms [69], however, this finding could be biased because we did not investigate the origin and prior movements of the vehicles, and if both vehicles used were properly cleaned and disinfected between uses. Thus, this finding also requires further investigation to clarify, including the type of animals transported in each vehicle evaluated.
Seasonality of human influenza is well studied. It is generally accepted that seasonal influenza in people peaks in the winter months and similar findings have been reported for swine [19] but causes of such seasonality are still not completely understood and little information is currently available to describe the pattern of IAV in swine over different seasons [70]. IAV prevalence in our study may be subjected of variation due to seasonality because of the long timeframe (from October 2016 to July 2017) to complete the sample collection, however, seasons do not occur in Colombia as a tropical area in which variations in climate factors between months are not strongly marked in comparison to temperate regions.
We acknowledge some limitations of our study. Selecting farms according to some pre-specified rules may have introduced selection bias in the study. Also, the inclusion of farms was based on the voluntary participation of farmers; and thus, selection bias was probably introduced. Lack of clarity and validity of the answers during analysis of survey data may include bias [71], but rigorous design and validation of the questionnaire can help to control biases. In our observational study, prior data collection, several training sessions and preliminary tests with farm owners (not included in the study analysis) were performed to reduce bias. The questionnaire was conducted by 6 veterinarians. Efforts were made to ensure the clarity of the questions during training sessions, however, the interviewers may have unintentionally influenced the responses, so interviewer bias cannot be completely excluded. Most of the questionnaires were closed, excepting those questions that could allow interviewees to provide more details answers or clarifications. Low response rate is another reported disadvantage of surveys [38], then questions with a minimum response rate of 70% are also required to reduce biases during the analyzes [72], however our study significantly exceeded that minimum response rate. Although the overall response rate was high, one cannot neglect the fact that some questions had a response rate under 95% and this could lead to answers non representative of the whole study area population, so bias could also be influencing our results. Additionally, compliance to the measures of biosecurity surveyed in the swine farms was a factor not assessed in our study and may represent a limitation. We acknowledge that farm biosecurity has evolved over time as swine diseases have been better understood, but effectiveness of farm biosecurity depends largely on the compliance by the personnel involved in the production system [42]. Thus, compliance of BP is always a challenging issue when analyzing biosecurity in animal productions. Poor biosecurity compliance has been reported in animal productions [73] and it is frequently related to lack of knowledge or comprehension of the biosecurity principles, but also to human dimensions such as personality and attitudes [74]. Also, the survey data was collected in a cross-sectional study from interviews which may have led to bias towards answers stating measures believed to be applied on farm rather than confirming measures really applied. It is known that perception of a given biosecurity measure apply can be also strongly influenced by multiple factors [75]. Finally, our work was based on a cross-sectional observational study, thus causal relationships should not be inferred from the results presented.