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Viability of Iberian x Meishan F₂ newborn pigs. II. Survival analysis up to weaning¹

J. Casellas^*, J. L. Noguera, L. Varona, A. Sánchez^*, M. Arqué and J. Piedrafita^*,2

^* Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain, and and Àrea de Producció Animal, Centre UdL-IRTA, 25198 Lleida, Spain

	Abstract

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Iberian x Meishan F₂ piglet’s preweaning survivability was analyzed using categorical data regression procedures within the proportional hazards assumption. A frailty sire model was assumed with the litter effect treated as an additional random source of variation. Moreover, the relative birth weight within litter and the litter effect were considered time-dependent covariates that changed their values in the second day of life due to cross fostering carried out to standardize litters. Six variables had a significant effect on survivability: birth weight (P < 0.01), relative birth weight within litter (P < 0.001), rectal temperature 60 min after birth (P < 0.01), type of presentation at birth (P < 0.05), presence of stillbirths (P < 0.001), and presence of mummified fetuses (P < 0.001). Small piglets (<0.98 kg) suffered a high hazard ratio (6.57; P < 0.001), with this variable being clearly lower for the rest of birth weight categories. Piglets that were small in relation to their siblings (relative birth weight within litter) also suffered an increased death risk, with a hazard ratio of 1.81 (P < 0.05), which was similar to animals with posterior presentations at birth (hazard ratio = 1.80; P < 0.05). Piglets with a rectal temperature lower than 35.4°C 60 min after birth showed the highest hazard ratio (7.18; P < 0.01). Furthermore, the presence of mummified fetuses decreased the survivability of the remaining siblings, with a hazard ratio of 2.03 (P < 0.01), as did the presence of stillbirths (hazard ratio = 3.55; P < 0.001). The inclusion of the two random effects allowed us to estimate the mode of the joint posterior density of the sire variance (0.08) and the litter variance (1.98). The estimated heritability of preweaning survival reached a value of 0.03. We conclude that piglet survival involves several systematic influences related to birth weight, thermoregulatory ability, and injuries suffered during gestation and farrowing. The genetic variance was small compared with those generated by the common environment, for which the genetic improvement of piglet survival seems difficult.

Key Words: Categorical Survival Analysis • F₂ Piglets • Preweaning Survival • Proportional Hazards

	Introduction

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Approximately 12% of the piglets born alive die before weaning and half these losses happen during the first 3 d of life (Varley, 1995). This preweaning mortality represents a significant economic loss, and many attempts have been made to identify the key factors involved in neonatal vulnerability. In recent decades, different measurements of survivability have been used, like the percentage of piglets weaned or dead, or the number of preweaning deaths (Rothschild and Bidanel, 1998; Knol el al., 2002), but these measurements suffer from a severe information loss because the piglets that died 1 d or 2 wk after birth were treated alike. In addition, linear models may be inadequate to analyze binomial data (alive or dead at weaning); however, these data may be studied through logistic regression. The development of the survival analysis techniques based on the proportional hazards model (Cox, 1972) and their implementation in available software packages, such as the Survival Kit (Ducrocq and Sölkner, 1994), has allowed for the study of the survival trait as a continuous variable. Furthermore, when we analyze a short period of time, the dates of failure seem rather broadly grouped, and the time scale has to be considered as discrete, with a decreased number of intervals. This is the usual situation in an analysis of the piglet’s preweaning survival, and then proportional hazards model techniques are a priori incorrect because they assume continuity of the baseline hazard distribution and absence of ties between ordered failure times (Cox, 1972). Nevertheless, Prentice and Gloeckler (1978) resolved these limitations by developing categorical data regression procedures within the proportional hazards assumption. In this context, this study was an attempt to analyze the factors that influence the survivability of piglets throughout the preweaning period by categorical data regression procedure within the framework of the proportional hazard models.

	Materials and Methods

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Data Source

This study included 99 litters from F₁ Iberian x Meishan primiparous sows of the Nova Genètica farm at Solsona (Catalonia, Spain). Mummified fetuses and stillborn piglets were excluded; thus, the analysis was carried out with 899 piglets born from November 2001 through May 2002. The sows were housed in total confinement during farrowing and lactation in standard farrowing crates within pens with an environmental temperature of 24°C, which were provided with heating plates (38°C) for piglets. Piglets were monitored for arterial oxygen saturation at birth (OS-0) and 1 h later (OS-1), heart rate at birth (HR-0) and 1 h later (HR-1), and rectal temperature at birth (RT-0) and 1 h later (HR-1) using a Vet/Ox 4404 monitor pulsoxymeter (Heska Corp., Fort Collins, CO). Given that piglets had black skin, heart rate and arterial oxygen saturation were evaluated with the tongue, using an Exotic Reflectance Sensor (Heska Corp.). The time elapsed between birth and the first udder contact (TU), as well as the first suckle (TS) were also recorded. Three variables were monitored to calculate the viability score, following in part the procedure described by Randall (1971): attempts to stand, onset of respiration, and muscle tone. Each variable was evaluated according to the classification proposed by Zaleski and Hacker (1993), and viability score was obtained as the sum of the three variables. The methodology followed in data collection has been extensively described in a companion paper (Casellas et al., 2004). Two classes of relative birth weight within litter were defined, in part following the classification proposed by Robert et al. (1995). The piglets were classified as within-litter small animals (WLS) when they weighed at least 150 g less than the litter mean birth weight or when they weighed 75 to 150 g less than the litter mean birth weight and at least 75 g less than the immediately larger member of the litter. The remaining newborn pigs were considered to be within-litter normal animals (WLN), with the exception of individuals that were classified within the missing value category because it was not possible to estimate the average weight of their litter. The missing value category included less than 10% of studied piglets.

Statistical Analysis

Piglet survivability was described by the observed survival function estimated empirically by the Kaplan-Meier method (Kaplan and Meier, 1958). This observed distribution was the result of the influence of different factors involved in preweaning viability. To investigate the influence of these factors, a categorical survival analysis has been carried out. In the case of discrete failure times, such as in the present study, survival analysis is applied following the procedure described by Prentice and Gloeckler (1978), with the time scale defined by the following intervals: [0 = t₀, t₁); [t₁, t₂); . . .; [t_–1, t). All failures that occur within the interval t_i–1, t_i are implicitly grouped, and we have to assume that censoring only occurs at the end of each interval. Then, under the proportional hazards model, hazard function h(_i; x_j) within interval _i = (t_i–1,t_i) of a particular piglet j, characterized by a set of explanatory variables x_j, is written as:

where h₀(_i) is the baseline hazard function and ß is a vector of regression coefficients (Cox, 1972; Prentice and Gloeckler, 1978). This technique does not make any assumptions for the baseline hazard function distribution, but it can be viewed as a fully parametric (exponential) model that included a time-dependent covariate that changes at each interval (Ducrocq, 1999). An interesting extension of the previous model is the addition of random effects. The resulting mixed model can be written as:

where u is a vector of random variables and z_j is the incidence vector. These models have been extensively described by Ducrocq and Casella (1996).

Model Selection for Fixed Effects

Birth weight, OS-0, OS-1, HR-0, HR-1, RT-0, RT-1, TU, and TS, originally continuous variables, were transformed into categorical variables with four arbitrary classes, low (L), medium-low (ML), medium-high (MH), and high (H), following the 0.15, 0.5, and 0.85 percentiles, and an extra class for the missing values, if they were present. The cutoff points for these variables are shown in Table 1. The remaining analyzed fixed factors were sex of the piglet, with two levels (male or female); presentation at birth, with three categories (anterior, posterior, or not registered); birth order, grouped according to six intervals: (first and second, third and fourth, fifth and sixth, seventh and eighth, ninth and tenth, and eleventh and more piglets); number of piglets born, with four categories (less than seven piglets, between seven and 10 piglets, between 11 and 14 piglets, and >14 piglets [mummified fetuses were not considered]); presence of stillbirths in the litter; presence of mummified fetuses in the litter; viability score, with categories 3 and 4 grouped together because the number of piglets was small; and oxytocin administration before piglet birth. Relative birth weight within litter was analyzed as a time-dependent variable that could change its value in the second day of life because of cross fostering carried out to homogenize litters. A preliminary analysis allowed us to determine effects that reached statistical significance (P < 0.05). Before rejecting the nonsignificant fixed effects, each of them was tested with the group of fixed effects initially significant to determine whether any became significant. Proportional hazards hypothesis was tested defining a different baseline survivor function (S₀) for each effect level and plotting the log(–logS₀) vs. log t. Under proportional hazards, computed lines for different levels should be parallel (Ducrocq et al., 2000).

To account for genetic sire effects (s_j) and random litter effects (l_k), the previously selected fixed-effects model was extended to a mixed model:

where s is the vector of additive sire effects, l is the vector of random litter effects, and z_1j and z_2k are the corresponding incidence matrices. The litter effect has been treated as a time-dependent covariate that can change its value at Time 2. These movements were carried out during the second and third day of life. The vector s of additive sire genetic effects, under polygenic inheritance, follows a multivariate normal distribution s MVN (0, A), where A is the additive genetic relationship matrix among sires, and the vector l of random litter effect was assumed to be log-gamma distributed following a single parameter , which can be understood as the variance of common environment (). The sire variance () and the variance of common environment (litter effect; ) were estimated using the Bayesian approach described in Ducrocq and Casella (1996). Multivariate normal priors for and log-gamma priors for were combined with the likelihood function of the data to obtain an expression proportional to the joint posterior density of all parameters. Further technical details of the random parameter estimation under survival analysis are given in Ducrocq and Casella (1996). Analyses were carried out using the Survival Kit package (Ducrocq and Sölkner, 1994). Heritability was calculated applying the formula of Yazdi et al. (2002).

	Results

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Of the 899 piglets studied, 117 died before weaning (13.0%). Fifty-eight (49.6%) and 17 deaths (14.5%) occurred during the first and second day of life, respectively, and the 42 remaining deaths were distributed throughout the preweaning period, until piglets reached 4 wk of life. The observed survival function estimated by the Kaplan-Meier method is presented in Figure 1. The decrease in the probability of survival was maximal on the first day of life (6.5%), and was substantially lower during the second day (1.9%). After the third day of life, the percentage of the daily reduction of the observed probability of survival became small and relatively homogeneous, oscillating between 0 and 0.6%.

View larger version (8K): [in this window] [in a new window]   Figure 1. Observed survival function estimated by the Kaplan-Meier method.

The survivability of piglets increased significantly with birth weight (Table 2). In this context, animals classified as of L (< 0.9 kg) birth weight had 6.57 times more risk of death than the H birth weight animals (P < 0.001) (Figure 2). The hazard ratios for ML (0.9 to 1.1 kg) and MH (1.1 to 1.3 kg) birth weight piglets were intermediate and significant (P < 0.05), with values of 3.13 and 3.73, respectively (Table 2). In addition, the probability of survival also decreased for the animals that were small in relation to their siblings (WLS), with a hazard ratio of 1.81 (P < 0.05; Table 2; Figure 2); and low temperatures 60 min after birth (<35.4°C) reduced the probability of survival (hazard ratio = 1.97; P < 0.01; Table 2). The posterior presentations at birth were more harmful for piglet survival (P < 0.05), and the presence of stillbirths or mummies reduced the survivability of the remaining siblings, with hazard ratios of 3.55 (P < 0.001) and 2.03 (P < 0.01), respectively (Figure 3).

View larger version (13K): [in this window] [in a new window]   Figure 2. Survival function of piglets with high (H) or low (L) birth weights considering whether they were small in relation to their siblings (WLS) or normal (WLN). The remaining significant factors were fixed to a high temperature 60 min after birth (above the 0.85 percentile), anterior presentation at birth, and absence of mummies and stillbirth within litter.

View larger version (13K): [in this window] [in a new window]   Figure 3. Survival function of piglets born in litters without stillbirth or mummified fetuses (All live), with stillbirths (Stillbirths), with mummified fetuses (Mummies), and with stillbirths and mummies (SM). The remaining significant factors have been fixed to medium-high birth weights (birth weight between the 0.5 and 0.85 percentiles), normal birth weight within litter, high temperature 60 min after birth (above the 0.85 percentile), and anterior presentation at birth.

View this table: [in this window] [in a new window]   Table 3. Estimates of variance components for sire () and litter ()

	Discussion

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Birth weight of the piglets influenced their survivability directly and indirectly. The probability of survival was maximized for the piglets born with weights greater than 1.3 kg and was minimized for the newborn pigs smaller than 0.9 kg, whereas pigs of intermediate weights also had intermediate probabilities of survival. Furthermore, an indirect effect has been observed in piglets that were small in relation to their siblings because they showed lower survivabilities independently of their absolute birth weight. Several authors have reported the direct effect of birth weight on piglet survival (Daza et al., 1999; Herpin et al., 2001; Quiniou et al., 2002), as well as the negative influence of within litter birth weight variation for the smallest piglets (Robert et al., 1995; Milligan et al., 2002a,b), but these factors had never been analyzed within the same model. The lowest probability of survival of small piglets could be caused by several factors: 1) they need to spend more energy for thermoregulation purposes because the greater surface to body mass ratio results in greater heat losses (Herpin et al., 2002); 2) they generally suffer from a delay in the first colostrum intake as well as always being a disadvantage when competing for the resources with their larger siblings (Tuchscherer et al., 2000; Casellas et al., 2004); and 3) they probably suffer a variable degree of physiological immaturity. On the other hand, when a piglet is small in relation to its siblings, the decrease observed in its survival probability is related to the disadvantage suffered in the competition for access to more functional and productive teats, as pointed out by Robert et al. (1995). These facts allow us to establish two ways to decrease preweaning mortality, closely related to the influence of birth weight. A long-term genetic improvement in within-litter variation in birth weight seems possible because heritabilities ranging between 0.08 and 0.12 have been described for this character under an animal model (Damgaard et al., 2003). Conversely, increasing the homogeneity of piglet weight within litters by cross fostering of some piglets allows direct action on the problem, decreasing the differences between the piglets, and thereby promoting more equitable access to the resources and reducing mortality (Marcatti Neto, 1986). Considering that 64.1% of deaths occurred during the first 2 d of life, homogenization of litters should happen as soon as possible. Alternative techniques like selective tooth clipping (Robert et al., 1995) may also be useful.

Only the piglets with low rectal temperatures 60 min after birth (lower than 35.4°C) suffered an increased risk of death. The importance of thermoregulation for piglet survival has been extensively studied (Herpin and Le Dividich, 1998) and has frequently been related to low birth weights. The joint analysis of birth weight and rectal temperature 60 min after birth allowed us to determine that, although small animals reached lower temperatures (Casellas et al., 2004), other factors have to influence the thermogenic capacity of piglets, like the early ingestion of colostrum (Herpin et al., 2002) or hypoxia suffered during delivery (Herpin et al., 2002). Cold piglets are more inactive and weak, and it decreases their capacity to accede to the teats and to ingest a sufficient volume of colostrum (Casellas et al., 2004). This cycle may only be solved in part with the early identification of piglets with thermogenic problems and by helping these animals during their first intake of colostrum, although it might not be economically viable.

The presence of stillbirths or mummified fetuses in the litter originated an increase in the respective hazard ratios (3.55 and 2.03). The causes of stillbirths are closely related to delivery and piglet resistance to asphyxiation (Knol et al., 2002), whereas the causes of mummified fetuses, while multiple and varied, act on the piglets during gestation, not during delivery (van der Lende and van Rens, 2003). The decrease in survival of piglets with mummified siblings could be related to two different groups of causes: 1) the injuries that cause the death of some fetuses also negatively influence the remaining piglets and 2) the presence of mummies can originate changes in the uterine environment that have repercussions on the postnatal viability of siblings. Several authors have analyzed the heritability of stillbirths but the estimates are very small (see Knol et al., 2002 for a review) and genetic improvement is not viable. Nonetheless, the problems that generate the presence of stillbirths can be decreased with correct vigilance and management during delivery (Varley, 1995), although it may not be viable from an economic standpoint. Posterior presentations at birth were clearly harmful for piglet survival, in accordance with Randall (1971) and Herpin et al. (1996), who related posterior presentations with a major degree of asphyxiation.

The effect of time elapsed between birth and TU, as well as TS, on piglet survival, did not reach statistical significance. Several authors have published positive relationships between the piglet’s survival and TU or TS (Herpin et al., 1996; Christison et al., 1997; Tuchscherer et al., 2000), but the nonsignificant values obtained in our work may be due to the influence that birth weight exercises over TU and TS variables (Tuchscherer et al., 2000; Casellas et al., 2004). Similar results have been obtained for the viability score, whose influence did not reach statistical significance, although various authors have related this factor to piglet survival (Randall, 1971; Zaleski and Hacker, 1993).

The estimate of the litter effect variance is clearly higher than that of the sires, which shows the importance of the common environment generated by the sow, siblings, and facilities. The estimated heritability was clearly low and similar to an estimate reported previously (Damgaard et al., 2003). An important result of this paper is the fact that variance components can be estimated precisely, with a small standard deviation, despite an extremely high censoring rate (87%). Unfortunately, similar results for preweaning mortality are not available, but low standard deviations for the estimation of additive variances have also been described in the laying hen survival analysis (Ducrocq et al., 2000). This good precision could be due to the size of the dataset and the good pedigree structure (Ducrocq et al., 2000), as well as the grouping of the censoring records at the end of the preweaning period.

	Implications

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The analysis of survival carried out has allowed us to determine factors that operate on the preweaning mortality of piglets, and results show the utility of survival techniques in this field. Birth weight influences the survivability both directly, as seen with the increased mortality with decreased birth weight, and indirectly, in that piglets that were small in relation to their siblings showed a higher risk of death. These results suggest the importance of an early homogenization of piglet weight within litters to equilibrate the competition among siblings, and also suggest that genetic improvement to homogenize birth weight may be possible. Low rectal temperatures 60 min after birth (<35.4°C) were clearly harmful. Furthermore, the presence of stillbirths and/or mummified fetuses influenced the viability of the remaining siblings, decreasing their survival. Unfortunately, the heritability estimate was low (0.03) and thus, the genetic improvement for this trait seems difficult.

	Footnotes

 
¹ Financial support was provided by Ministerio de Ciencia y Tecnología, Spain (Grant AGL2000-1229-C03). The authors are indebted to the staff of Nova Genètica for cooperating in the experimental protocol, in particular to F. Márquez, I. Riart, R. Malé, F. Rovira, M. González, and E. Ramells, and to M. Fina and J. Tarrés for their active and friendly collaboration during data collection. The authors gratefully acknowledge the contributions of the INRA (France) and the SIA El Dehesón del Encinar (Spain) for providing the purebred Meishan sows and Iberian boars, respectively. The English revision by C. Simmons is also acknowledged.

² Correspondence—phone: 34935811399; e-mail: jesus.piedrafita{at}uab.es.

Received for publication November 7, 2003. Accepted for publication March 2, 2004.

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