The study revealed some important factors to be associated with high pleuritis values in pig herds in a country with a low prevalence of many respiratory diseases. A protective effect of “farrow-to-finish” herd type compared with “only finishing pigs” herds emerged regarding odds of having higher pleuritis values in meat inspection. In previous studies, contradictory results have been obtained when herd type has been considered in association with pleuritis. Several studies have found that herds that purchase weaners (i.e. finishing pig herds) have a greater risk for respiratory diseases than herds with closed production [2, 12]. On the other hand, farrow-to-finish herds have been observed to have greater odds for having chronic pleuritis in slaughtered pigs [13, 14]. A fairly recent study by Jäger et al. [13] found that if growers are purchased from more than three different farms the protective effect of finishing herd over farrow-to-finish herd was diminished. In addition to herd type, the AIAO production system has been associated strongly with better respiratory health and, more specifically, lower prevalence of pleuritis [13, 15]. In our study, AIAO production correlated strongly with herd type, and because of this the variable was omitted from multivariate modelling. It is not at all surprising that finishing pig herds were able to empty the piggery at one time and farrow-to-finish herds utilized more continuous flow of animals. In our study, AIAO production was significantly associated with pleuritis in univariate analysis, but surprisingly, case herds utilized more often the AIAO system than control herds. When we investigated only the effect of AIAO production on level of pleuritis and built a statistical model solely for this purpose, our data (results not shown) revealed that herd type acts as confounding variable. When confounding is taken into account, AIAO production seems to have a protective (albeit not significant) effect on pleuritis level.
The present study agrees with the previously described debilitating effect of growing herd size on prevalence of pleuritis [5, 6, 7, 14, 16]. This effect could be related to infection pressure because bigger herds are more likely to need to purchase more animals, which is accompanied by an increased risk of introducing pathogens or naïve animals into the herd. Disease dynamics (i.e. better possibilities for spread and maintenance of airborne infection) also differ between bigger and smaller herds [12]. Considering environmental risks, it might also be more difficult for bigger herds to control optimal air quality, especially if the compartments are large. However, this was not the case in our study, where room size did not differ between large and small herds.
Herd type and size are commonly and inextricably associated with specific types of management practices, e.g. farrow-to-finish herds do not buy weaners or growers and finishing herds more commonly are able to practice AIAO production. However, the herd type itself, when used in the sense of sourcing animals, seems to be too vague a definition for choices made on a certain farm regarding infection pressure. To be able to reliably predict risk factors for chronic pleuritis, these management practices need to be defined in more detail. Earlier studies have been able to find several risk factors for pleuritis in the slaughterhouse such as number of pigs per pen [2], pig density in the neighbourhood [3, 4], low health status of the herd [3], poor biosecurity [4], lack of disinfection of the farrowing room [6], no cleaning and disinfection [13], lack of complete AIAO production [3, 13, 17], mixing of pigs [3, 13], season [3, 4, 18], mean temperature below 23 °C in the finishing unit [6], low weaning age [2] and low airspace [4]. Jäger et al. [13] observed that keeping pigs with more than a one-month age difference in the same airspace acted as a risk factor for pleuritis. In our study, we observed a marked weight difference (approximately 40–50 kg) among finishing pigs kept in the same room, which had an association with pleuritis in univariate analysis but did not remain in the multivariate model. However, the weight difference was more marked in control than case herds. The weight difference registered was a visual approximation between the smallest and the biggest pig in the room, and no actual weighing of pigs was used. This kind of method might have accentuated extreme observations and might contain considerable error. Cleveland-Nielsen et al. [3] found that feeding only dry feed protected the herd from high pleuritis values. However, some studies have not observed any association between non-infectious risk factors studied and pleuritis in the slaughterhouse [1, 19]. Some of the significant associations between pleuritis and management or health factors have been found only in univariate analyses, while in multivariable analysis the associations have disappeared. This highlights the need to be able to model several correlated variables simultaneously. Hurnik et al. [15, 20] attempted to overcome these kinds of problems typical for surveys by using a factor analysis. Regarding pleuritis, they found only one common observed feature of herds having greater prevalence of pleuritis: extensive type of farming. However, there is convincing evidence in the literature, as summarized above, that management choices related to infection pressure have a considerable effect on prevalence of chronic pleuritis. This is further supported by the observation of the protective effect of farrow-to-finish herd type and small herd size in our study.
Flank biting was more prevalent in case herds than in control herds in the present study (2.6% vs. 0.04%); moreover, in the regression model flank biting tended to be associated with higher pleuritis prevalence of the herd (OR 9.6, p = 0.05). No previous studies have reported an association between flank biting and meat inspection findings in slaughtered pigs. However, some studies have recognized the association between tail biting, another vice common in pigs, and prevalence of pleuritis. Tail biting seems to act as a risk factor for pleuritis found in meat inspection [21, 22, 23]. The prevalence of both acute and healed tail biting was higher in our case herds than in control herds. However, the difference was not statistically significant, probably because of marked variation between herds and a lack of statistical power. The association between behavioural vices and disease susceptibility is thought to be mediated through stress, which in general is considered to affect immunity negatively, thus making the host animal more susceptible to diseases [24]. Recent evidence suggests that environmental factors, namely enriched housing, probably via decreased stress, can reduce susceptibility to, for instance, co-infection with porcine reproductive and respiratory virus (PRRRSv) and APP [25]. Furthermore, it is unlikely that a respiratory pathogen, such as APP, is transmitted by flank or tail biting. It is more plausible that flank or tail biting share similar risk factors with pleuritis, e.g. overcrowding, lack of straw, large group size, climatic extremes, feeding type and poor ventilation [26]. In addition, Klinkerberg et al. [27] and Tobias et al. [28] have carried out simulation and transmission studies and have concluded that APP outbreaks are unlikely to be caused by spread of the pathogen, but more likely to be a consequence of clinical signs triggered in pigs already infected. The observed tendency in our study that links flank biting with chronic pleuritis supports the importance of management factors in avoiding stress in commercial pig farms, in this way decreasing susceptibility to respiratory disease.
Most studies investigating pleuritis have been done in countries with many possible pathogens present in the pig population. In Finland, the prevalence of porcine respiratory pathogens differs from the situation in continental Europe. The country has been free from Aujeszky disease virus (ADV), porcine respiratory corona virus (PRCV) and porcine reproductive and respiratory syndrome virus (PRRSV) for decades and nearly free from Mycoplasma hyopneumoniae (MHyo) [10]. Porcine respiratory disease complex is a multifactorial syndrome with clinical signs caused usually by multiple micro-organisms, both bacteria and viruses together with environmental and management-related factors as well as genetics [29]. Both viruses and MHyo have been considered as primary pathogens, which predispose pigs to concomitant bacterial infections such as APP [29]. Earlier, seropositivity to MHyo was reported to increase the odds of contracting chronic pleuritis [14], and APP serotype 2 together with PRRSV was described to be significantly associated with pleuritis [30].
We found a tendency for high prevalence of APP2 antibodies in a herd to be associated with higher prevalence of pleuritis (OR 7.8, p = 0.1). However, this was not the case for APPIV antibodies. Controversial results have been previously reported regarding serology and pleuritis registered in meat inspection [2, 9, 14, 31]. In case of endemic disease, serology might not be the most useful tool in diagnosis or at least paired samples are needed [10]. Although we observed high pleuritis values and high prevalence of APP antibodies, APP might not be the main causative infectious agent in our study herds. Also, the timing of the infection remains unknown in our study, as we are dealing with endemic disease. For example, Wallgren et al. [5] found different serological patterns during the growing time of pigs in four herds. They showed that repetitive sampling helped to pinpoint the actual causative agent and disease pattern in different herds. In addition, APP serotypes vary not only in commonness, but also in virulence. In Finland, the virulent serotype 2 has been found to be common throughout the country [32], causing also acute respiratory infections (10). Fablet et al. [30] noted that of the APP serotypes only serotype 2 was associated with pleuritis. Hence, it seems logical that antibodies, especially against serotype 2, tend to be associated with high pleuritis values. APxV toxin is produced by all APP serotypes and this might explain why the association of APPIV antibodies with pleuritis went undetected.
In our study, only five herds had antibodies against SIV. Even though four out of these five herds were case herds and only one a control herd, the total number of SIV-positive herds was too small for statistical modelling. SIV, both H1N1 and A(H1N1)pdm09, was first found in Finland in 2008 and 2009 [33], and both of the strains have thereafter spread throughout the country. The arrival of this new pathogen to a previously naïve population may have played a role in the increased pleuritis prevalence observed in meat inspection during recent years. However, based on current results, it remains unclear whether SIV plays a role in chronic pleuritis in Finland.
Different composition of primary respiratory pathogens may have an impact on the interplay of different risk factors and secondary pathogens such as APP. Without major primary pathogens, finishing pigs might be able to withstand some deficiencies in management without acute or chronic illness. Thus, in an observational study in a naïve country like Finland, the effect of risk factors might need to be more prominent than in other countries to cause detectable alterations in outcome.
In this study, herds were defined as cases or controls based on detection of pleuritis for a longer time period than only one batch. Furthermore, we gathered management-related data during herd visits, which enabled us also to inspect the pigs clinically. These choices should have made both the allocation of herds into cases or controls and the collection of data reliable. The results should be more valid than in studies where allocation has been done based on a single batch of slaughtered pigs or where the data have been collected with questionnaires sent to farmers or telephone interviews [3, 13, 17]. Purposive sampling was utilized in our study to highlight the differences between case and control herds. Typically, this type of sampling leads to valid estimates if otherwise conducted properly, but might restrict extrapolation of study results. Study herds had on average 1000 finishing pigs, which is approximately double the typical Finnish pig herd size. However, 50% of finishing pigs in Finland are produced in herds having more than 1000 finishing pigs. While descriptive statistics may not be representative of all Finnish pig herds, the results may well be applied in modern pig production. As always in a case-control study, the role of cause and consequence cannot be proven. However, most of the recorded factors were linked to reasonably permanent features of the study herds and present over a prolonged time period, and thus, could thought to precede the observed MI findings.
Almost 70% of herd owners asked to participate in the study were willing to be involved. As control herds were overrepresented amongst opt-out herds, there is a possibility of selection bias in the data. The most frequent reason for opting out was “not willing to participate”. Almost 60% of these herds were case herds and the reason for refusal was frequently that “participation will not help them to overcome the high pleuritis problem that they have”. The majority of opt-out control herds no longer had pigs, and this was the second most common reason for opting out. Generally, opt-out herds had a larger herd size than opt-in herds. Therefore, it is possible that the herds not willing to participate in the study might have been not only larger, but also not as keen to take active measurements to improve their management. This kind of perceived attitude may have some (possibly negative) influence on overall management of herds. Possible selection bias may have caused study herds to resemble each other more in terms of influential management variables than they actually do in the source population, leading to an underestimation of the severity of risk factors.
Lack of statistical power might have caused several factors observed to differ between case and control herds to be seen as statistically non-significant. For example, for the present sample size (at least 24 herds in both groups) the presumed difference in exposed and non-exposed herds should have been 40% and 80% in most influential risk factor (herd type). The realized distribution (36% vs. 80%) fulfilled that criteria well and statistical significance was seen. However, as calculated for another categorical variable (NH3 concentration), the observed proportions of exposed and non-exposed herds were 61% and 52%, respectively, which may lead in a lack of statistical power.