The findings in our study support the alternate hypothesis that despite being a largely subclinical disease (<4% morbidity in 194 viral positive pigs), influenza A(H1N1)pdm09 virus infection reduced the pigs’ growth performance in terms of FCE and hence ADG. Consequently the infected pigs needed more time to reach 100 kg and additional feed was also consumed. The negative growth performance effects were most evident in the pigs that were infected at a young age, as shown by the VIR1 pigs. The negative effects of reduced FCE and ADG in VIR1 extended into growth phases two and three. That was beyond the viral shedding period of SIVs of about seven days [7]. We have no explanation why adverse effects in growth performance appeared only during the post viral period and lasted longer in these VIR1 pigs that were infected at bodyweight before 60 kg bodyweight. The duration of negative effects were shorter in the pigs infected at a later age as represented by VIR2 pigs. Negative effects in these pigs were limited to GF2, the same growth phase that they were tested positive for the virus. Twenty two of the 123 VIR1 pigs were in the upper weight range of 50 kg and 60 kg. If these pigs were in the early viral shedding period of 7 days during testing, shedding of virus and presumably the manifestation of adverse effects on growth performance could cross over to the next growth phase (61-80 kg). Misclassication bias could then result in concluding the delayed and extended negative effects of the virus infection on FCE in VIR1 pigs (Table 3). However in our bias analysis (Table 4), the removal of these twenty-two heavier pigs, from VIR1 and leaving 101 younger pigs (VIR1a) that shed virus between 33 kg and 50 kg did not change the result. Just as in the original VIR1 group of 123 pigs, the adverse effects on growth performance of these 101 younger pigs (VIR1a) appeared during the post viral period of growth phase two (61 -80 kg) and deteriorated during growth phase three (81 -100 kg).
Interestingly, none of the infected groups had depressed average feed intake in any of the three growth phases as we expected because anorexia is listed as one of the clinical signs in pigs infected with the classical SIVs. Instead the pigs infected at a young age (VIR1) ate more during growth phases two and three, which were the post viral shedding period for VIR1 pigs. Even though they increased their feed intake, it was insufficient to compensate for the reduced FCE during growth phases 2 and 3 to raise their daily growth high enough to catch up with the seronegative pigs. Consequently the overall feed intake of these VIR1 pigs increased by 8 kg but were still slower by 1.6 days in getting to 100 kg bodyweight. Despite their lower FCE, the increased appetite of VIR1 pigs during the post viral shedding period of GF2 and GF3 was enough to allow them to reach the bodyweight of 100 kg earlier than VIR2 and VIR3 pigs which were infected at a later age.
Landrace and Duroc are the two major breeds in Norway. The proportions of both breeds were almost equal in our study. Even though there were intrinsic breed differences in growth profile with Landrace having a better feed conversion efficiency and higher daily feed intake and hence higher daily growth, our investigation found no differential adverse effects on growth performance caused by this virus infection on the two breeds.
Although the time period of infection for the 874 pigs in the SEROPOS group were unknown, these pigs could have been infected at any time during their growth phase before or after arriving at the boar station. Blood for cELISA tests were collected from these pigs when they were about 100 kg. With the exception of five pigs (67 days for the youngest) in the SEROPOS group, the remaining 869 pigs were older than 12 weeks when they were tested for antibodies. It was therefore unlikely that they harboured detectable maternal antibodies [24]-[26] at the time of testing. The observation that the SEROPOS pigs had similar results to the 37 virus positive pigs in the VIR3 group in that the adverse effect on FCE and ADG occurred during GF3 points to the possibility that the majority of seropositive pigs were infected at the older age like VIR3 pigs. Although these SEROPOS pigs reached 100 kg bodyweight at the same time as the SERONEG pigs, these SEROPOS pigs had reduced FCE during growth phase three which resulted in these pigs consuming an additional 2 kg feed to reach the bodyweight of 100 kg.
Bias analysis on misclassification
The cELISA test had a sensitivity and specificity of 93.7% and 99.1% (Manufacterers data sheets). Although these values are considered high for test performance, given the largely lack of clinical picture in this disease for corroboration, there could nevertheless be a small number of SERONEG and SEROPOS pigs that were misclassified and hence biased the adverse effects towards the null. Our quantitative bias analysis using Episens [23] for adverse effects on a dichotomized FCE showed that the odds ratio for a poorer FCE if the pig was SEROPOS versus SERONEG was 1.13. The odds ratio after adjusting for misclassification bias was 1.3, a change of 2 percent. The small bias of 2 percent in our study was towards the null (OR= 1).
The literature [7] states that SIVs in general, cause near if not 100% morbidity when they infect naïve pigs. An experimental study with influenza A(H1N1)pdm09 virus by Brookes et al. in 2010 [9] reported 100% morbidity involving 19 pigs in their experimental study, where 2 pigs were infected by contact. In contrast, we found in our field study that influenza A(H1N1)pdm09 virus infection of Norwegian pigs was largely subclinical, with only six (<4%) in a sample of 194 virus positive pigs reported to have clinical signs. Apart from coughing, clinical signs, like nasal discharge, were so mild that only through closer observation or handling of the pig, for example nasal swabbing, could the signs be detected. The mild clinical signs and low morbidity recorded during the observation period were similar to the morbidity experienced by other Norwegian pig farms infected with influenza A(H1N1)pdm09 virus for the first time [15],[16]. This shows that despite Norwegian pigs having no cross-protective immunity [27] to other strains of SIVs, the A(H1N1)pdm09 virus experienced in Norwegian pigs appeared to be of low pathogenicity that caused no or only mild clinical signs.
In recording the clinical signs at the pig testing station, possible bias of focusing on pigs with anorexia (shown on the computer records) may have led to underestimating the morbidity of the disease because we found no statistically significant decrease in appetite in the infected pigs in the stipulated three growth phases. A transient drop in appetite for one or two days would be masked by the three growth phases since the intervals in each growth phase were longer than two days and if there were compensatory increases in feed intake following one or two days of depressed feed intake.
Favourable conditions in our study
Pigs in our study did not have co-infections of other subtypes of influenza A viruses, M. hyopneumoniae, PRRSV, Aujesky’s disease virus and porcine respiratory coronavirus, given Norway’s disease free status for these pathogens [12]. Secondly, the daily recordings of feed intake and bodyweight for each pig were computerised without the presence of human interference. This ruled out human bias or error in making measurements to provide accurate calculations of the performance parameters. Thirdly, the repeated measures allowed the study of progressive effects of the virus in pigs infected at different ages and also the duration of the negative effects on growth performance.
Validity of study design and statistical models
With 34 or more pigs in each of the five groups of pigs, the sample sizes are considered large for multi-level models [28]. The five statistical models were based on maximum likelihood in estimating the predictors that allow for inference to the Norwegian pig population.
It was appropriate to use multi-level analysis because of the hierarchical nature of the data. The data, having 5865 observations nested in the 1955 pigs, which in turn were nested in the 43 herds, were handled to account for the variations between individual pigs and between the various herds including unmeasured confounders like other infections at the herd or individual level. Pigs from the study sample of 43 herds were represented in the reference group of 887 seronegative pigs. Consequently, the effects of virus infection (primary predictor of interest) and the known covariates (predictors we wanted to control) were more accurately estimated. Such hierarchical models solved some of the problems mentioned in a similar study by Straw et al. [18], in that this study was designed to control heterogeneities due to the environment, herd health status, host characteristics and management conditions inherent at the pig and herd level to reduce if not eliminate confounding [29],[30]. Furthermore, keeping the pigs at one location in a uniform environment and husbandry eliminated these factors as potential confounders in our model. Our models also proved to have a relatively high explanatory ability on the variance as the achieved adjusted R2 were 51% (ADG), 59% (FCE), 51% (Age100 kg), 66% (ADFI) and 20% (OFI), which were proportions of variation that were explained by the predictors in the models. The longitudinal nature of the data for each pig allowed the statistical models to account for changes to FCE, ADFI and hence ADG with respect to the stage in the pig’s growth phase by including growth phase (GF) as a dummy variable in the statistical models thus controlling for confounding due to normal variation of feed conversion efficiency and daily feed intake with stage in growth phase.
Our samples of 1955 pigs included pigs tested at the station over four years from 2009 and 2012. We saw in our models that birthdate was a significant covariate because pigs born later had better growth performance as a result of improvement over time due to genetic selection, improved feed, and management improvement. Pigs belonging to the five infection status groups were disproportionately distributed over these four years since all 194 virus positive pigs were sampled in a single year (2011) while 560 pigs from seronegative and seropositive group were sampled in 2009 and 2010. Despite these disproportions, we were able to account for the marginal effects attributed to improvement over time by including birthdate as a covariate in our multi-level regression models. This allowed us to increase the study sample and hence the power of our study.
As normal occurrence and also seen in our study, the feed intake increases and FCE declines as a pig grows. Despite the reduced FCE in an older pig, its ADG is still higher because it consumes a higher amount of feed than the younger pig. Hypothetically, if older pigs ate the same amount of feed as the younger pigs, their ADG would be lower because of a reduced FCE as depicted by the coefficients of GF for outcome ADG and FCE in Tables 2, 3 and 4. This is also depicted in our marginal plots at Figures 2 and 3. This again underlines the importance of having growth phase as a dummy variable in our models to ensure comparisons of our outcomes between the 5 infection-status groups were valid because comparisons between groups were made in the same growth phase.
The coefficients of covariates breed, birthdate, growth phase and average daily feed intake in all five models were useful for the validation of our statistical models by comparing their values with other sources of pig performance data. An improbable coefficient would have raised a red flag on the models.
All five outcomes in our models were correlated. The calculation of feed conversion efficiency was based on average daily feed intake and average daily growth recordings. They in turn determined the remaining 2 outcomes on overall growth performance, which were age of pig at 100 kg bodyweight and overall feed intake. These latter 2 outcomes on overall growth performance are especially useful in evaluating economic consequences of the infection for farmers. Cost of extra feed and a delay in getting the pig to the market will lead to higher overheads (feed and veterinary costs) and lower income for the farmer since fewer pigs are sold in the fixed time period. We found that pigs infected when they were young (33 kg - 60 kg) required an additional 8 kg feed and were 1.6 days slower in reaching 100 kg bodyweight. Farmers can estimate the added operational costs if they know that their pigs were infected at a young or older age.
In other parts of the world, SIVs seldom act alone, but with concurrent infections to cause porcine respiratory disease complex where SIVs are the most common primary pathogens [1],[2],[7],[31],[32]. Hypothetically, the severity of influenza A(H1N1)pdm09 virus infection in terms of growth performance would be aggravated by concurrent infection of these other respiratory pathogens [7]. On the other hand, these pigs could also be protected from influenza A(H1N1)pdm09 virus infection because of presence of protective immunity against other strains of SIVs [27].
