Sow mortality risk factors at different reproductive stages: an analysis of production data

Sow mortality risk factors at different reproductive stages: an analysis of production data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sow mortality risk factors at different reproductive stages: an analysis of production data Junhao Huang, Osvaldo Fonseca, Lola Pailler, Lorena Correa, Antonio Martínez, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8865197/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Sow mortality has been increasing in recent years, posing significant challenges to commercial pig production. High mortality affects animal welfare, farm productivity, and causes substantial economic losses. While previous studies have identified risk factors such as high parity, seasonal effects, and farrowing-related complications, limited research has quantified mortality risk at different reproductive stages in Spain. This study aims to analyze sow mortality patterns and identify associated risk factors. Results Analysis of production records from 16 Spanish farms revealed that 55.59% of sow deaths occurred before farrowing, while 44.41% occurred after farrowing. Mortality risk peaked during late gestation (105–118 days) and again during the first and after the fourth weeks post-farrowing. Several risk factors were identified at farm and individual levels. Internal gilt replacement was associated with increased mortality risk. Before farrowing, sows with parity ≥3 and single-service sows exhibited higher mortality. After farrowing, increased mortality was observed in parity 1 sows and those with repeat breeding. Seasonal effects showed highest mortality rates in sows bred in spring and farrowing in summer. Matched case-control analysis revealed that parity 0 sows had a younger age at first service, and deceased sows had shorter gestational length, fewer piglets born, born alive, and a higher incidence of stillbirth fetuses at their last litter before their death. Comparing their lifetime performance, dead sows had younger gilt age at first service, fewer parity at removal, higher average number of piglets born, born alive, born still and born mummified per parity, but they had fewer weaned piglets and nonproductive days. Conclusions This study identifies late gestation and the first week postpartum as critical windows for sow survival, warranting enhanced peripartum care. Internal gilt replacement and parity extremes emerged as key mortality risk factors. Seasonal vulnerability around summer farrowing underscores the need for environmental mitigation strategies. Repetitive services may signal underlying health issues and increasing postpartum mortality risk. These patterns emphasize integrating early reproductive indicators into sow management and culling decisions. Parity reproductive performance farrowing risk stillbirths mummified fetuses gilt management herd longevity sow death causes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background The increasing mortality rate of breeding sows has been rising at an alarming rate in recent years, with significant implications for various aspects of farm operations [ 1 , 2 ]. In herds across the USA, Canada, Australia, and the Philippines, sow mortality reached 13.56% in 2021, following a steady rise from 7.32% to 11.78% between 2012 and 2018 [ 3 ]. This upward trend in sow mortality has resulted in productivity losses, particularly as a large proportion of these deaths occur during gestation, leading to significant piglet losses per sow per year. The financial burden associated with sow mortality extends beyond direct losses, encompassing replacement costs, reduced reproductive output, and potential disruptions in herd management. Replacing a deceased sow can be costly, especially when gilt availability is limited, sometimes necessitating the retention of sows that would otherwise be culled. Additionally, gestational losses further amplify economic setbacks, with total expenses per lost sow estimated at $ 400– $ 500 [ 4 ]. A sow reaches positive net income around the third parity [ 5 ], and profit per litter rises with each subsequent parity by $ 100– $ 200 [ 6 ]. Thus, early loss or culling leads to considerable economic loss, with studies estimating an accumulated profit gap of $ 360– $ 441 between sows producing three and six litters.[ 6 ]. Herd-level risk factors for sow mortality are influenced by a range of biological and environmental elements. Key factors include farm-specific conditions like housing, nutrition, and health management [ 7 – 11 ]. Feeding practices influence sow mortality by affecting digestive and metabolic stress [ 12 ]. High ambient temperature and humidity markedly increase sow mortality by overwhelming heat-dissipation mechanisms, leading to cardiovascular failure, especially during poorly ventilated, humid summer periods without nighttime cooling or acclimatization [ 13 ]. Biosecurity management is a key determinant of infectious disease occurrence. Among swine diseases, porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most critical, raising the death rate by approximately 14% compared with naïve or stable breeding herds [ 14 ], causing an estimated annual economic loss of $ 380.82 million in the U.S. breeding sector from 2016 to 2020 [ 15 ]. Sow-level factors include sow characteristics such as reproductive performance, age at first farrowing, and parity, along with genetic breed [ 2 , 3 ]. Sows with a younger age at first farrowing and larger first-litter size were found to have longer lifespans [ 16 ]. In high-parity sows, mortality is elevated from torsions of abdominal organs, prolapse, and cystitis-pyelonephritis, while gilts and first-parity sows are vulnerable due to physiological immaturity, pelvic constraints, and stress associated with first estrus, mating, and parturition [ 17 ]. Breed affects sow mortality through susceptibility to PRRSV infection [ 18 ], viral replication in alveolar macrophages [ 19 ], gut microbiota composition [ 20 ], and immune response capacity [ 21 , 22 ]. Sows undergo marked physiological and metabolic changes across reproductive stages, particularly between gestation and the post-farrowing period [ 23 , 24 ]. During late gestation and early lactation, sows experience substantial shifts in energy balance, endocrine regulation, immune function, and physical load [ 24 ]. Previous studies have reported different distributions of diseases across reproductive stages. For example, lameness was reported of showing differing prevalence or incidence patterns across gestation and lactation [ 25 – 27 ]. Similar stage-dependent patterns have also been observed for other disorders, including reproductive complications, metabolic diseases, and infectious conditions [ 28 , 29 ]. As established contributors to sow mortality, these conditions may contribute to the previously reported variation in the frequency and distribution of sow deaths across reproductive stages. Consistent with these physiological differences, sow mortality shows stage-dependent variation. However, most existing studies have focused on counts or proportions of sow deaths, rather than on incidence rates that account for the duration of exposure within each reproductive stage [ 10 , 17 , 30 , 31 ]. The contribution of reproductive stages to sow mortality risk has not been fully quantified. Similarly, the influence of sow-level and herd-level factors on mortality across reproductive stages, when assessed using incidence rates rather than absolute counts, has not been systematically investigated. Filling this gap could provide more precise identification of sows at higher risk of death and inform targeted management strategies. In commercial pig production, data management and recording systems are essential for monitoring productivity and health. However, many farms still rely on manual record-keeping, resulting in inconsistent or incomplete data. Studies have shown wide variation in data quality across farms depending on herd size, management software, and personnel training [ 32 , 33 ]. Moreover, in most cases, these records are mainly used for routine purposes, such as generating sow cards, task schedules, or brief performance overviews, rather than being analyzed to extract meaningful insights [ 34 ]. When systematically analyzed, herd data can provide a powerful foundation for evidence-based management, enabling producers and veterinarians to enhance sow lifetime reproductive potential and overall production efficiency [ 35 , 36 ]. Spain is one of the most important swine production countries in the world, with almost 35,000,00 pigs produced in 2023 [ 37 ]. However, studies over sow mortality are rare in this region [ 8 , 38 ], particularly using large-scale data. Therefore, given the observed stage-dependent variation in sow mortality, the limited availability of large-scale production data, and the scarcity of studies in Spain, this study aims to analyze sow mortality trends across reproductive stages in 16 Spanish swine farms over a six-year period and to identify key factors contributing to elevated mortality rates and individual sow mortality risk using production records. Methods Dataset preparation Production data from a Spanish swine production company were used in this study. These records were collected daily as part of the farms’ routine management procedures. The database included production data from 16 breeding herds conveniently selected by the producer. These farms were chosen based on the company's prior experience, as they provided more detailed and consistent classification of sow mortality compared to the rest of the farms in the system. Data management, descriptive statistics and analyses were performed using R studio [ 39 ]. Definitions and metrics In this study, a sow is defined as a female pig that had been inseminated at least once. Parity was defined as the number of farrowing completed, meaning that parity increased only after successful farrowing. A service is defined as any insemination event occurring during estrus and could include one or multiple mating events within the same estrus cycle. The period of study is comprised between January 2019 and December 2024. Gilts that were culled before their first service were excluded from the analysis, since not all farms consistently recorded such removals. The dataset used for this study was composed of five files from the production record system with sow individual records. Variables used from each file are summarized in Table 1 . Table 1 Summary of the files and fields used to create the data frames analyzed in this study Files Fields FARM CENSUS Sow ID, Entering Date, Parity At Farrowing, Farrowing Date, Elimination Date, Elimination Type (Death, Euthanasia or Culling), Cause of Elimination, Breed, Non-Productive Days At Elimination. LITTERS BORN Sow ID, Parity, Farrowing Date, Total Born Piglets, Live Born Piglets, Dead Born Piglets, Mummies, Adopted Weaned, Weaned Piglets, Breed. BREEDING INFORMATION Breeding Date, Sow ID, Parity, Breeding Number, Previous Breeding Date. NEWLY BREED GILTS Breeding Date, Result, Type, Sow ID, Expected Farrowing Date, Discharge Date, Age at First Breeding. Sows can be eliminated by culling, death or euthanasia. The death of a sow was also categorized as accident, illness, peripartum, prolapse, sudden death, ulcer, locomotor problems, evicted or clostridium. This latter categorization is done by farm personnel following the company guidelines: Accident: deaths resulting from trauma or physical injury, such as crushing, falls, or entrapment within housing facilities. Illness: deaths caused by clinically diagnosed diseases not falling into other specific categories, including systemic infections or chronic conditions. Peripartum: deaths occurring around the time of farrowing, often associated with complications of parturition or immediate postpartum physiological stress. Prolapse: deaths resulting from vaginal, uterine, or rectal prolapse, typically occurring during late gestation or shortly after farrowing. Sudden death: deaths without prior clinical signs, characterized by abrupt onset and rapid progression. Ulcer: deaths associated with gastric or gastric-proximal intestinal ulceration leading to hemorrhage or perforation. Locomotor problems: deaths attributable to lameness, fractures, arthritis, or other musculoskeletal disorders impairing mobility. Clostridium: deaths with compatible clinical signs of infection with clostridium species (e.g. bloated). Seasons were defined according to conventional meteorological periods [ 40 ]: Winter (from December 1st to February 28/29th ), Spring (from March 1st to May 31st ), Summer (from June 1st to August 31st ) and Fall (from September 1st to November 30th ) The risk of death of sows was calculated as the number of dead sows divided by the number of removed sows, multiplied by 100. Annualized mortality incidence rates (expressed as cases per 1000 sow-years) were calculated by dividing the number of sow deaths by the total sow-years at risk and multiplying the result by 1000 sows [ 41 ]. Sow-years at risk were calculated from the date of first service to the date of removal. For sows that were still alive at the time of data extraction, sow-years at risk were defined as the time between the first service and the last recorded event (e.g. service or farrowing). Based on established criteria from previous studies, certain records were identified as extreme values and treated as missing data (the value was replaced by NA and not taken account in the analysis). These included: gilt age at first service below 160 days or above 400 days (11,008 records; [ 42 ]); gestation lengths shorter than 105 days or longer than 125 days (73 records; [ 43 ]); and total piglets born fewer than 1 or greater than 30 (173 records; [ 44 ]). Additionally, other extreme values were excluded, such as: cases where piglet deaths exceeded the total number of piglets born plus those fostered to the sow (1,486 records); weaned piglet counts for sows nursing piglets from other dams (37,594 records); piglets weaned numbering fewer than 1 or exceeding 30 (14,931 records); and nonproductive sow days of 366 days or more (94 records). Descriptive analysis For the overall weekly mortality analysis, death counts and average weekly sow population were aggregated by week, and weekly mortality was calculated as the ratio of deaths to population. Shaded and labeled background bands were used to represent winter, spring, summer, and autumn. Temporal trends were smoothed using a LOESS function, and individual weekly mortality points were plotted. X-axis labels were formatted by date, with major breaks every six months and minor breaks every month. This visualization allowed examination of both weekly mortality fluctuations and seasonal patterns over the study period. Weekly sow mortality and pregnancy-week mortality were analyzed using recorded sow deaths and sow population data from 2019 to 2024. For the pregnancy-week analysis, each sow was assigned a pregnancy month based on the time elapsed from service, and weekly death counts and sow-years at risk were aggregated by week and pregnancy month. Mortality incidence per week was calculated as the number of deaths divided by the sow-years at risk. Incidence rate analysis Using the GLIMMIX procedure, two-level Poisson regression models were applied: 1. To estimate the incidence rate of death while accounting for herd variability and to analyse its associations with herd-level factors. The herd-level factors that we examined were herd size, piglets weaned per sow per year (PWSY), and whether the farm bred their own gilts. Herd size was calculated as the average annual inventory of sows in the farm for each year during the study period. For each sow, the number of days spent on the farm within the given year was calculated based on entry and removal dates. These days were summed across all sows in the farm and divided by 365.25 to convert the total into sow-years, representing the average herd size for that year. PWSY was annually calculated from 2019 to 2024 and then were averaged for each farm in six 1-year periods. To achieve this first objective, an intercept-only Poisson models with an offset and no fixed or random effects were applied to estimate simple incidence rates, and random effects were added later to estimate overall and herd-level incidence rates, accounting for herd effects, along with their 95% confidence intervals (CI). and random effects were added later to estimate overall and herd-level incidence rates with their 95% confidence intervals (CI). From this model, the random herd effect with its standard error was also obtained. Additionally, intraclass correlation coefficients (ICC) were calculated as the proportion of variance explained by herd-level differences, using the R package “iccCount”. In this study, herd-level management factors were included in the analysis, with gilt replacement strategy (own vs. external) modeled as a fixed effect, and numerical indicators such as herd size and pigs weaned per sow per year (PWSY) evaluated using Pearson’s correlations with herd-level incidence rates. 2. To examine the associations between the incidence rate of death and parity-level factors. Assessed parity-level factors included weeks from service or delivery, parity at service or delivery, re-service, service or delivery season. To address the second objective, parity-level factors were added to the models as fixed effects in a univariate manner. For analyses of time from service or farrowing, individual records were expanded into week-level risk sets, covering the period from each reproductive event (service or farrowing) until the next event or sow death. Weekly mortality outcomes were recorded, and sow-years at risk were calculated for each interval to account for the time at risk in the models [ 45 ]. Since farrowing is a major risk factor for sow mortality, the model was applied separately to two distinct time periods: the period from insemination to farrowing and the period from farrowing to the next insemination. The number of deaths and the logarithm of sow-years at risk divided by 1000 were set as the outcome variable and the offset, respectively, to predict the number of deaths per 1000 sow-years. Matched case-control study Three matched studies were performed to investigate differences between dead sows (cases) and their matched controls, defined as sows that left the herd for reasons other than death or that remained alive: Age at first service was compared between gilts that died before their first parity and their matched controls (gilts that did give birth to the first parity). Controls were matched on parity, farm, number of services, year of service, and season. Litter reproductive performance was compared between case parity records (last parities before sow death) and matched control parity records from sows that did not die after the corresponding parity. Matching was based on parity number, farm, farrowing year, and season. Variables included gestation length, total piglets born, stillborn piglets, mummified fetuses, and piglets weaned. This analysis aimed to identify potential pre-death signals in litter performance. Lifetime performance metrics were evaluated by comparing deceased sows with their matched controls (those alive or removed for reasons other than death) to assess differences in long-term productivity. The average litter performance was determined by averaging the sow's lifetime totals across the parity. The controls were matched with the case based on the farm, first service year and season. The R package “ccoptimalmatch” was used in this study to match each case with up to four controls. Although not all cases could be matched with four controls, the majority were successfully matched at this 1:4 ratio, which has been shown to maximize cost-effectiveness and statistical efficiency [ 46 ]. Results The dataset from the 16 herds comprised 488,849 service records from 106,351 sows. Out of 106,351 sows, 76,607 sows were removed during the study period. The removal reasons were culling (71.6%; 54,848 sows), dead (21.7%; 16,634 sows), euthanasia (6.4%; 4,877 sows), and others (0.3%; 248 sows). The average annual mortality rate for the 16 farms was 5.20% (95%CI : 4.45% − 5.95%), 7.06% (95%CI : 6.11% − 8.00%), 6.80% (95%CI : 5.76% − 7.83%), 8.15% (95%CI : 6.58% − 9.71%), 9.82% (95%CI : 7.73% − 11.91%), 9.74% (95%CI : 7.42% − 12.06%) from 2019 to 2024, respectively. A summary of each farm yearly mortality can be found in Fig. 1 . The overall causes of mortality (dead) during the studied period were accident (5.42%; 901 cases), clostridium (3.75%; 624 cases), evicted (8.73%; 1453 cases), illness (9.70%; 1613 cases), locomotor problems (0.73%; 122 cases), peripartum (10.45%; 1738 cases), prolapse (10.00%; 1664 cases), sudden death (45.17%; 7514), ulcer (2.71%; 451 cases), or others (2.94%; 489 cases). The distribution of death causes from each year is shown in Fig. 2 . As shown in Fig. 3 , weekly mortality rates exhibited an overall upward trend, especially after 2022, with noticeable seasonal peaks during the summer and winter months. The descriptive statistics for the different factors used for the farm- and sow-level analysis are presented in Table 2 . The average herd size and PWSY during the period 2019–2024 were 1998.89 sows and 23.73, respectively. Table 2 Descriptive statistics of herd and sow-level factors used for the analysis in the 16 selected farms. SD: Standard deviation, IQR: Interquartile Range. Measurements N Mean SD Median IQR Range Herd level data Herd size 16 1998.89 2005.78 1288.8 (379.68 − 2197.91) 452.74 − 8749.46 PWSY 16 23.73 2.05 23.71 (22.51 − 24.92) 21.28 − 29.25 Sow-level data Parity at removal 106351 4.31 3.11 4 (1.5 − 6.5) 0 − 15 Nonproductive sow days 76587 45.34 50.29 26 (-6.5 − 58.5) 0 − 359 Age at first service, days old 103403 275.6 37.09 270 (245.5 − 294.5) 160 − 401 Total piglets born alive per sow 88954 58.59 38.77 51 (21.5 − 80.5) 0 − 278 Total piglets weaned per sow 88954 49.03 33.21 43 (19 − 67) 0 − 283 Days from last delivery to death 7387 16.62 19.75 13 (3.5 − 22.5) 0 − 529 Days from last service to death 9247 78.73 38.88 92 (58.5 − 125.5) 0 − 777 Parity-level data Number of parities at service 106442 3.87 3 3 (0.5 − 5.5) 0 − 16 Gestational length 380527 115.32 1.4 115 (114.5 − 115.5) 105 − 125 Piglets born alive 344745 13.94 3.91 14 (12 − 16) 0 − 30 Piglets born still 344745 1.33 1.87 1 (0 − 2) 0 − 27 Piglets born mummified 344745 0.51 1.24 0 (-0.5 − 0.5) 0 − 25 Piglets weaned 291485 79.91 42.77 73.33 (61.25 − 85.42) 3.57 − 2400 Service-level data 291635 11.93 2.59 12 (11 − 13) 1 − 30 Number of services 439788 1.11 0.36 1 (1 − 1) 1 − 9 Table 3 details the risk and proportion of death removals across parities. 15.64% of records (16,634 out of 106,351) indicated removal due to death. The risk of death was highest in the parity 6 + group (5.9%), followed by parity 1 (3.1%). Gilts were the ones with the lowest death risk (1.6%). It increased to 3.1% at parity 1 and then steadily decreased with increasing parity, remaining relatively low through parity 5 (2.4%). However, a marked increase was observed in the parity 6 + group, where the risk rose sharply to 5.9%. Table 3 Risk and proportion of death by parity at removal. Parity group Number of dead sows Sows at risk Removed sows Risk of removal due to death, % 1 Proportion of death, % 2 0 1700 106351 5717 1.6 29.7 1 3123 100634 10555 3.1 29.6 2 2479 90079 7805 2.8 31.8 3 2227 82274 7456 2.7 29.9 4 1844 74818 6841 2.5 27 5 1636 67977 6775 2.4 24.1 6 or higher 3625 61202 31459 5.9 11.5 Total 16634 106351 76608 15.64 21.71 1 Denominator was the number of sows at risk. 2 Denominator was the number of removed sows. Among the total sow deaths (16,634), 44.41% (7,387) occurred after farrowing, while 55.59% (9,247) happened before farrowing (Fig. 4 ). The weeks with the highest mortality rates were as follows: 105–111 days of gestation, accounting for 6.93% (1,153/16,634) of deaths; 112–118 days, the peak period, with 21.57% (3,588/16,634); 119–125 days at 11.91% (1,981/16,634); 126–132 days at 6.89% (1,136/16,634); and 133–139 days at 8.63% (1,435/16,634). The highest mortality incidence was observed in pigs during their last month of pregnancy, with a pronounced peak occurring specifically in the summer (Fig. 5 ). The contrast between the last month of pregnancy and the other months, as well as the difference between summer and the other seasons, became more evident after 2022. The overall sow mortality incidence rate across the 16 farms was 99.26 deaths per 1,000 sow-years (95% CI: 97.84-100.71), as shown in Table 4 . The simple estimated mortality rate for serviced sows was 67.44 (95% CI: 66.12–68.78), while farrowed sows had a much higher rate of 242.5 (95% CI: 237.7-247.36) deaths per 1,000 sow-years. When considering median incidence rates across herds, serviced animals had a median of 62.866 (95% CI: 56.04–70.520), whereas farrowed sows had a median of 193.831 (95% CI: 158.748-236.668) deaths per 1,000 sow-years (Table 4 ). The ICC for these rates were found to range from 99.85% to 99.93%, indicating high herd-level variability. To further explore herd-level factors, a fixed factor was included in the model to assess the effect of self-breeding of gilts. Results showed that farms that bred their own gilts had a significantly higher mortality rate in farrowed sows (p-value 0.0140) and observation in total duration (p-value 0.0126). Additionally, Pearson’s correlation analysis, presented in Table 4 , herd size and PWSY did not show a significant association with mortality rates at any stage. Table 4 Incidence rate of death in serviced and farrowed sows in 16 farms. Measurements Duration From service until farrowing From farrowing until subsequent service Total duration Number of cases 9247 7387 16634 Number of sows at risk 106351 95255 106351 Sow-years at risk 137112.69 30461.33 167574.02 Incidence rate cases per 1000 sow-years (95% CI) 1 Simple estimate (95% CI) 67.44 (66.12, 68.78) 242.5 (237.7, 247.36) 99.26 (97.84, 100.71) Estimate taking herd effect into account (95% CI) 62.866 (56.043, 70.520) 193.83 (158.75, 236.67) 87.795 (77.11, 99.96) Model intercept (SE) 4.141 (0.06) 5.267 (0.10) 4.475 (0.07) Random herd effect (SD) 0.0515 (0.23) 0.1603 (0.40) 0.0681 (0.26) ICC, % 2 99.85 99.88 99.93 Incidence rate if the farms grow their own gilts (p-value) 76.48 (0.07) 299.87(0.01) 117.06 (0.01) Pearson’s correlation coefficient with herd management measurements (p-value) Herd size 0.23 (0.40) 0.44 (0.09) 0.36 (0.17) PWSY 0.24 (0.37) 0.40 (0.12) 0.37 (0.15) 1 The Simple estimate reflects the sow-level incidence, while the herd-adjusted estimate represents the median rate across herds. 2 The sow-years at risk was set to 1000 sow-years in the ICC calculation. ICC: intraclass correlation coefficients Death incidence in serviced sows was associated with the weeks from the last service, number of parities, number of services and service season (Table 5 ). There is a 3.69-fold increase at 14–15 weeks (101.085 cases per 1,000 sow-years), a 20.65-fold increase at 16–17 weeks (565.35 cases per 1000 sow-years) and a 8.66-fold increase at 18 weeks and more, compared to the baseline incidence observed at 0–1 weeks (27.372 cases per 1000 sow-years). The mortality incidence rate initially decreases with increasing parity but then rises again. Individuals at parity 2 exhibited the lowest mortality incidence. The highest rates are observed at parity 5 (71.969 cases per 1,000 sow-years) and parity 6 or higher (68.516 cases per 1,000 sow-years), where they reach 1.22 and 1.16 times the incidence rate of parity 0 (59.049 cases per 1,000 sow-years), respectively. Sows that underwent repeated services had a pre-farrowing mortality incidence rate (55.81 cases per 1,000 sow-years) that was 0.87 times that of sows serviced only once (63.95 cases per 1,000 sow-years). The mortality incidence rate for sows serviced in the spring was the highest compared to other seasons, at 73.66 cases per 1,000 sow-years. Table 5 Estimated incidence rate (cases per 1,000 sow-years) for death of serviced sows prior to farrowing, using the cohort data. Variable 1 Number of cases Number of records Total sow-years at risk Incidence rate (95% CI) Overall 9247 488849 137112.68 62.87 (56.04, 70.52) Weeks from service, p-value < 0.01 0–1 (0–13 days) 546 488849 18658.69 27.37 (23.89, 31.36) a 2–3 (14–27 days) 839 488047 18166.41 43.21 (38.80, 48.13) c 4–5 (28–41 days) 738 484671 17082.74 40.44 (36.20, 45.17) bc 6–7 (42–55 days) 761 476567 16454.39 43.32 (38.81, 48.35) c 8–9 (56–69 days) 625 470119 16061.84 36.47 (32.51, 40.91) b 10–11 (70–83 days) 655 465355 15770.48 38.94 (34.76, 43.63) bc 12–13 (84–97 days) 841 461607 15523.83 50.80 (45.61, 56.58) d 14–15 (98–111 days) 1750 458368 15263.98 107.53 (97.68, 118.37) e 16–17 (112–125 days) 2313 454331 3826.07 565.35 (515.00, 620.63) f 18 or more (over 126 days) 179 444153 304.25 544.33 (459.50, 644.81) f Number of parities at the service, p-value < 0.01 0 1700 95148 26722.52 59.05 (52.19, 66.81) ab 1 1403 83982 23220.89 56.31 (52.46, 60.44) a 2 1254 70510 19796.98 59.19 (55.02, 63.67) abc 3 1184 59736 16953.68 65.41 (60.74, 70.46) bcd 4 1017 50696 14368.94 66.43 (61.46, 71.80) cd 5 916 42394 11950.16 71.97 (66.41, 77.99) d 6 or higher 1773 33893 24099.51 68.52 (64.10, 73.24) d Number of Services, p-value < 0.01 First service 8127 396687 118800.16 63.95 (56.93, 71.83) a Re-service 1120 92162 18312.53 55.81 (48.9, 63.7) b Service season, p-value < 0.01 Spring 2800 121650 35454.87 73.66 (64.53, 84.07) c Summer 2146 122176 34860.53 57.44 (50.25, 65.67) a Autumn 2306 125097 33967.58 63.26 (56.1, 71.34) b Winter 1995 119926 32829.71 56.67 (49.55, 64.82) a CI: confidence interval. a−f Estimates within a group with different letters are different (P < 0.05). 1 The variables were analyzed univariately in the model. Table 6 , similar to Table 5 , presents the associations between fixed factors and mortality incidence in farrowed sows, specifically examining the effects of weeks from delivery, parity number, service times and season. The incidence of sow mortality was highest during the first week after farrowing (week 0), reaching 278.72 cases per 1,000 sow-years. This rate declined in the subsequent weeks but began to rise again after week 2. Notably, from week 7 onward, the mortality incidence exceeded that of the immediate post-farrowing period, with weekly rates of 393.80, 323.90, 354.53, and 455.41 cases per 1,000 sow-years recorded during weeks 7 to 10 and beyond. Except for parity 1 (212.27 cases per 1,000 sow-years), mortality incidence in all other parities remained below the overall incidence rate (193.83 cases per 1,000 sow-years). The difference in mortality incidence between first-service and re-serviced sows is statistically significant (p < 0.0001), indicating a higher mortality rate in re-serviced sows compared to those first-serviced. Seasonal effects were evident, with the highest mortality incidence recorded in sows that farrowed during the summer (225.41 cases per 1,000 sow-years). Table 6 Estimated incidence rate (cases per 1,000 sow-years) for dead sows after farrowing without subsequent service, using the cohort data. Variable 1 Number of cases Number of records Total sow-years at risk Incidence rate (95% CI) Overall 7387 488849 30461.33 193.83 (158.75, 236.67) Weeks from delivery, p-value < 0.01 0 (0–6 days) 2537 488849 7238.93 278.72 (228.37, 340.18) e 1 (7–13 days) 1326 485225 7160.67 147.32 (119.41, 181.76) b 2 (14–20 days) 1130 482622 7066.13 127.23 (103.01, 157.16) a 3 (21–27 days) 1218 479111 5326.58 182.22 (147.61, 224.95) c 4 (28–34 days) 452 471135 1797.63 206.06 (164.83, 257.61) cd 5 (35–41 days) 240 462405 735.39 263.92 (207.75, 335.29) de 6 (42–48 days) 126 488104 425.86 240.53 (184, 314.44) cde 7 (49–55 days) 109 451975 223.18 393.8 (298.59, 519.36) f 8 (56–62 days) 57 448573 139.84 323.9 (232.91, 450.43) def 9 (63–69 days) 45 445457 100.25 354.53 (248.34, 506.13) ef 10 or higher (over 70 days) 147 442646 246.88 455.41 (351.14, 590.63) f Number of parities at delivery, p-value < 0.01 1 1720 93374 6406.61 212.27 (173.3, 260) b 2 1225 81365 5110.02 192.16 (154.88, 238.4) ab 3 1043 68812 4346.01 193.5 (155.76, 240.37) ab 4 827 58740 3673.13 182.17 (146.32, 226.8) a 5 720 50031 3105.56 187.53 (150.38, 233.84) ab 6 or higher 1852 41427 7819.99 187.85 (151.78, 232.5) a Number of Services, p-value < 0.01 First service 6633 396687 27994.67 190 (155.77, 231.77) a Re-service 754 92162 2466.67 237.54 (192.05, 293.81) b Delivery season, p-value < 0.01 Spring 1650 90939 7355.37 180.03 (145.32, 223.04) a Summer 2355 103266 8326.77 225.41 (182.25, 278.8) b Autumn 1789 100064 8017.87 178.51 (145.64, 218.79) a Winter 1593 86331 6761.32 187.97 (151.7, 232.92) a CI: confidence interval. a−f Estimates within a group with different letters are different (P < 0.05). 1 The variables were analyzed univariately in the model. Based on the high-risk periods for sow mortality identified in this study, the causes of death were categorized and are presented in Fig. 6 . Among sows that died between 105–111 days after service, sudden death was the most prevalent cause, followed by prolapse and peripartum. During 112–118 days after service, the most common cause became peripartum, followed by sudden death and prolapse. In the 0–6 days post-farrowing period, peripartum complications were still the leading cause, followed by prolapse, sudden death. For sows that died 21–27 days post-farrowing, sudden death was the most frequent cause, followed by eviction and accident. Lastly, 7 weeks after delivery, the distribution shifted toward more external or environmental causes, such as accidents, eviction, while sudden death still contributed to a measurable fraction of deaths. Table 7 presents the comparisons of nine reproductive performance measurements between deceased sows and their matched controls. For the sows that died before giving birth for the first time, they had a significantly younger age at first service (p < 0.01). Among sows that died post-farrowing, their final parity was characterized by a shorter gestation length (p = 0.014), fewer total piglets born (p < 0.01), fewer live-born piglets (p < 0.01), and a higher incidence of stillbirths (p < 0.01). Although the overall number of piglets weaned did not differ significantly between groups (p = 0.337), a significant difference was observed in the number of mummified fetuses (p < 0.01). However, stratified analyses revealed contrasting results: the number of mummified piglets was not significantly different (p = 0.807), while the number of piglets weaned showed a significant difference (p < 0.01). Table 7 Comparisons of reproductive performance between case records and matched control records in matched case-control study. Reproductive performance Case Control p-value 7 Number of cases Mean (SD) Number of controls Mean (SD) Gilt age at first service, days old 1 1254 271.238 (35.845) 2646 3 278.102 (33.618) 0.000 (0.001) Gestational length, days 2 7382 115.398 (0.877) 29496 4 115.457 (0.877) 0.014 (0.000) Total piglets born 2 6584 15.036 (4.704) 26304 5 15.848 (2.498) 0.000 (0.000) Number of piglets born alive 2 6584 12.447 (4.991) 26304 5 13.858 (2.364) 0.000 (0.000) Number of piglets born still 2 6584 2.000 (2.797) 26304 5 1.424 (1.077) 0.000 (0.000) Number of piglets born mummified 2 6584 0.589 (1.371) 26304 5 0.567 (0.727) 0.000 (0.807) Number of piglets weaned 2 2062 12.024 (3.440) 8230 6 12.008 (1.845) 0.337 (0.000) 1 Cases (the first service records of sows that received the first service but died before they could give birth) and controls (the first service records of sows that didn’t die before their first parity) were matched by parity, farm, number of services received, service year and season. 2 Cases (the last litter records of sows that died after the parity) and controls (the litter records of sows that didn’t die after the parity) were matched by parity, farm, number of services received, service year and season. 3 610 cases have 1 control, 216 cases have 2 controls, 108 cases have 3 controls, and 320 cases have 4 controls. 4 4 cases have 1 control, 4 cases have 2 controls, 12 cases have 3 controls, and 7362 cases have 4 controls. 5 4 cases have 1 control, 4 cases have 2 controls, 12 cases have 3 controls, and 6564 cases have 4 controls. 6 4 cases have 1 control, 2 cases have 2 controls, 2 cases have 3 controls, and 2054 cases have 4 controls. 7 p-values for the stratified analyses are shown in the parentheses. Overall, deceased sows showed distinct lifetime performance patterns compared to their matched controls (Table 8 ; P < 0.01), including higher average numbers of total and live-born piglets per parity, but also increased stillbirths and mummified fetuses, and fewer piglets weaned. Additionally, deceased sows had a younger age at first service and fewer nonproductive days. Stratified analyses further supported these associations. Table 8 Comparison of lifetime performance between case sows and matched control sows Lifetime performance Case 1 Control 2 p-value 8 Number of cases Mean (SD) Number of controls Mean (SD) Gilt age at first service, days old 16243 272.488 (35.589) 59098 3 274.136 (26.897) 0.000 (0.000) Parity at removal 16634 3.420 (2.582) 60635 4 5.043 (2.170) 0.000 (0.000) Average number of piglets born per parity 12943 14.398 (4.407) 47219 5 13.836 (4.226) 0.000 (0.000) Average number of piglets born alive per parity 12943 12.640 (4.196) 47219 5 12.192 (3.800) 0.000 (0.000) Average number of piglets born still per parity 12943 1.245 (1.471) 47219 5 1.162 (0.721) 0.000 (0.000) Average number of piglets born mummified per parity 12943 0.514 (0.983) 47219 5 0.482 (0.470) 0.000 (0.000) Average number of piglets weaned per parity 11117 9.611 (3.595) 41323 6 9.659 (3.406) 0.001 (0.000) Nonproductive sow days 16454 49.707 (54.031) 47774 7 51.119 (39.332) 0.000 (0.000) 1 Case sows were sows that died 2 Control sows were alive or removed for non-mortality reasons. The controls were matched with the case based on the farm, first service year and season. 3 73 cases have 1 control, 1,034 cases have 2 controls, 3,587 cases have 3 controls, and 11,549 cases have 4 controls. 4 68 cases have 1 control, 1,036 cases have 2 controls, 3,625 cases have 3 controls, and 11,905 cases have 4 controls 5 10 cases have 1 control, 783 cases have 2 controls, 2,957 cases have 3 controls, and 9,193 cases have 4 controls. 6 352 cases have 2 controls, 2,441 cases have 3 controls, and 8,324 cases have 4 controls. 7 2,640 cases have 1 control, 3,789 cases have 2 controls, 2,544 cases have 3 controls, and 7,481 cases have 4 controls. 8 p-values for the stratified analyses are shown in the parentheses. Discussion In this study, sow mortality was observed to increase generally during the 6-year period from 2019 to 2024 in Spain. This increasing trend is a broader global pattern. For instance, in the United States, the sow mortality rate has also shown a steady rise over recent years, with reported rates of 12.31% in 2019, 13.91% in 2020, 14.86% in 2021, 14.54% in 2022, and 14.675% in 2023[ 47 ]. Notably, the mortality rates observed in this study were lower than those reported in the U.S. during the same period. This is consistent with benchmarking studies suggesting that Spain generally exhibits better swine production performance compared to the U.S.[ 37 ] Regarding the timing of sow deaths, mortality incidence was higher post-farrowing than during gestation. Peak rates observed between 105–118 days of gestation (near farrowing), and in the first weeks after farrowing, aligning with findings from previous studies [ 10 , 38 ]. These findings suggest that late gestation and farrowing are the most critical and high-risk event for sow mortality, emphasizing the need for intensive monitoring of pregnant sows during late gestation. Typically, sows are recommended to be moved to the farrowing site no later than day 110 of gestation [ 48 ]; however, based on these results, an earlier relocation at day 105 may be more appropriate. Notably, a sharp increase in mortality incidence was observed after 4 weeks post-farrowing, a pattern also reported in studies of lameness and prolapse incidence [ 25 , 45 ]. However, based on standard production cycles, most sows are weaned during the third week post-farrowing and typically return to enter estrus for the next breeding cycle. As shown in Table 6 , the sow-years at risk dropped markedly after the fourth week, indicating that the majority of sows had already been bred and exited the post-farrowing phase. Therefore, sows remaining at risk beyond week 4 likely represent a subset with reproductive issues, illness, or management decisions against further breeding. This suggests that the elevated mortality after week 4 is not due to post-weaning timing itself but rather reflects underlying conditions in a vulnerable subpopulation. In other words, the increased mortality is likely a consequence of health or reproductive failure, not a risk factor inherent to the post-weaning period. In the period 7 weeks after delivery, the incidence of death appears elevated. However, as shown in Fig. 6 , the leading causes of death in this time window, aside from sudden death, were accidents, evictions, and illness, suggesting a different risk profile compared to earlier periods. Notably, prolapse is rare, while causes linked to systemic illness or external events dominate. This pattern supports the interpretation that sows remaining at risk beyond week 4 are likely to represent a selected subset with reproductive failure, health issues, or were intentionally removed from the breeding herd. Thus, the high mortality rate is not inherently due to the post-weaning timing but rather reflects the underlying vulnerability of this subgroup. The causes of death during these critical periods remain poorly documented in our dataset. This is a common phenomenon across the swine industry. For example, in a study evaluating the accuracy of sow culling classifications, 23% (209/923) of farm-reported culling codes were found to be inaccurate [ 31 ]. In our result, sudden death predominated across most time points, likely due to the lack of post-mortem examinations, which are often cost-prohibitive. Although the category "peripartum" refers to deaths caused by dystocia—a condition of difficult or prolonged farrowing—it may clinically manifest as sudden death prior to piglet expulsion, particularly during unsupervised nighttime farrowing. In such cases, without direct observation, it is nearly impossible for farm personnel to distinguish between death due to dystocia and sudden death, leading to potential misclassification in recorded data. Similarly, "illness" is a broad classification, indicating that farmers noticed abnormalities in the sow before death and may have even attempted treatment, but without a definitive diagnosis. The term "eviction" is also ambiguous and does not clearly indicate a cause of death. Among the primary causes of sow mortality, prolapse stands out as the most well-defined and least likely to be misdiagnosed or overlooked. This degree of inaccuracy was believed that it may cause severe limitations for studies that rely on farm-reported assessments of clinical conditions. Given these limitations, farmers should receive better training to improve their ability to monitor and document sow health conditions. Keeping more detailed records before sudden deaths occur could provide valuable supplementary information for diagnosis. Additionally, farmers should be equipped with basic skills to conduct preliminary assessments, helping to identify potential health risks earlier. In other studies, the peripartum period was described to have more than 50% of sow deaths [ 10 , 17 , 49 ], and the possible reasons are heart failures, genitourinary lesions, and prolapses based on post-mortem diagnosis [ 10 ]. Necropsy-based investigations have further revealed that mortality in sows often involves diverse underlying conditions. For example, one study reported positional changes in internal organs (32% of 100 deaths), arthritis (19%), and urogenital disorders (7%) as the most frequent findings in dead sows [ 50 ]. Several stressors, such as heat stress, aggressive interactions, and increased body weight, have been identified as contributing factors to peripartum mortality, particularly due to heart failure [ 13 , 49 , 51 , 52 ]. In addition to cardiovascular issues, urinary tract infections are a leading global cause of culling and sudden death in sows [ 53 ]. Heavier pigs and those with lameness issues have a higher likelihood of ascending infections because their lower body parts are more likely to come into contact with surfaces contaminated by feces [ 54 , 55 ], while sows in late gestation due to their increased weight have more likelihood to urinary tract infections. Floor types that have better drainage effects should be considered. Both gestation and farrowing are significant risk factors for prolapse, as the reproductive process weakens connective tissues, making prolapse a major concern. Studies have identified parity, litter size, and piglet birth weight as contributing factors, with pelvic organ misalignment resulting from structural weakening [ 56 ]. In humans, pelvic organ prolapse has been linked to low bone mineral density [ 57 ], and postmenopausal women are at higher risk due to estrogen deficiency. Similarly, among late-gestation sows, those with lower body condition scores have shown a higher incidence of prolapse compared to those in normal or overweight conditions[ 58 ]. Given these findings, optimizing prenatal nutrition, particularly calcium supplementation, may help reduce the risk of prolapse. In our study, the weaning period (typically the third week post-farrowing) exhibited a noticeable peak in the number of sow deaths, with sudden death being the most frequently recorded cause. However, when adjusted for sow-years at risk, the mortality incidence during this period was not exceptionally elevated. It is important to note that "sudden death" is a broad classification that lacks specificity regarding the underlying pathological mechanisms. To gain deeper insight, we referred to a previous study that reported a 5.35% mortality rate (6 out of 112 sows) during the weaning-to-estrus interval. In that study, confirmed causes of death included heart failure (2 cases), liver lobe torsion (2 cases), and vaginal or rectal prolapse (2 cases) [ 30 ]. Notably, two-thirds of these cases could potentially explain some of the sudden deaths observed in our study. However, due to the small sample size of that study, it does not fully capture the range of potential causes during this period, nor does it clarify the true composition of sudden deaths in our dataset. Several factors may contribute to increased mortality at weaning, including the stress of separation from piglets, hormonal fluctuations associated with the return to estrus, and stress induced by relocation. These factors could exacerbate underlying health conditions, ultimately leading to sudden death. For the herd-level risk factors, although internal gilt replacement is commonly associated with enhanced biosecurity, our findings indicate that farms employing this strategy had higher sow mortality rates after farrowing and throughout the reproductive cycle. Importantly, this association was not confounded by herd size or productivity (PWSY), as neither factor showed a significant relationship with mortality in our data. This suggests that the elevated risk may stem from other herd-level dynamics. One plausible explanation is that internal replacement systems require careful management of gilt development, and any shortcomings, such as inadequate body condition, improper age at first service, or insufficient acclimatization, may predispose sows to peripartum stress and health issues. Additionally, limited genetic diversity or suboptimal selection within closed herds may allow physically weaker individuals to enter the breeding population, increasing vulnerability during critical reproductive stages. These findings underscore the importance of managing replacement gilt quality, not just quantity, in herds practicing internal replacement. Our results indicate that sows beyond parity 6 face the highest overall mortality risk, likely due to age-related health deterioration and increased susceptibility to disease. High parity sows had a higher mortality incidence rate after service and a rather lower incidence rate after farrowing than other parities. Meanwhile, parity 1 sows also experienced elevated mortality, primarily due to a higher post-farrowing death rate than in any other parity. This may stem from the physiological immaturity of young sows, as their bodies are still developing and may not yet be fully prepared for pregnancy and farrowing. Mortality incidence declines with parity, reaching its lowest point at parity 4, before rising again in later parities. In contrast, pre-farrowing mortality is lowest in parity 1 sows but increases steadily with advancing parity. Mortality risk varies across studies depending on the parity cycle phase but is generally highest in low-parity sows or those at parity 6 and beyond [ 1 , 4 , 38 , 49 , 59 – 61 ]. One possible explanation for this discrepancy is differences in culling strategies across studies. In our study, farms may have lower culling efficiency for older sows, meaning that more high-parity sows remain in the herd until they die, rather than being removed pre-emptively. In contrast, studies with more aggressive culling policies may observe fewer deaths in older sows simply because they are removed before reaching a critical state of health. Service history appears to be associated with differential mortality risk across reproductive stages. Sows that underwent repeat services exhibited a lower mortality rate during the gestation period. One possible explanation is that the additional time between services may allow these sows more opportunities to recover physically before conceiving again. However, previous studies have associated a longer weaning-to-service interval with increased culling rates and reduced farrowing proportions [ 62 , 63 ]. This may be because in those studies, sows with poorer reproductive health either failed to conceive at the first service or were deliberately delayed by farm managers, reflecting underlying health concerns that predisposed them to culling or reproductive inefficiency. Since our study focuses exclusively on mortality (not culling), the lower mortality observed in repeat-service sows may partly reflect a survivorship bias—sows with evident reproductive issues might have already been removed from the herd before death. Supporting this interpretation, our post-farrowing mortality analysis revealed that sows with repeat services had a higher incidence of death after farrowing, suggesting that reproductive dysfunctions reflected by repeat services may manifest more clearly as health risks in the postpartum period. Several studies have established that high mortality rates in both winter and summer are an industry-wide concern [ 49 , 59 , 60 , 64 – 67 ]. In the results, sows bred in spring and those farrowing in summer had the highest mortality rates. Given that pre-farrowing deaths were concentrated in the final one to two weeks of gestation, the mortality peak for spring-bred sows still occurred in summer. This suggests that high summer temperatures were a primary factor driving sow mortality in our study. This seasonal temperature pattern likely explains the significantly higher mortality rates in summer compared to winter. Specially, since 2022, summer heat waves were constantly reported in Europe [ 68 , 69 ]. Spain, located in the southern Europe, suffered the most from the global warming among European countries because of its drought in summer [ 70 , 71 ]. According to the State Meteorological Agency (AEMET), 2023, 2022, and 2024 were the first, second and third-hottest years [ 72 – 75 ].Most of the farms included in the studies were from the north east of Spain, including Catalonia, Aragon and Valencia. This part of Spain experienced the hottest summer in 700 years in 2022 [ 76 ]. These heat waves have caused numerous human fatalities [ 77 ] and, unsurprisingly, also significantly impacted livestock production [ 78 ]. All this summing up explained and matched the extreme mortality peak after 2022 in the summers we observed in Fig. 3 . According to the matched case-control study, sows that died had a higher number of stillbirths. Infectious diseases should be considered in this context. For example, PRRSV is a well-documented cause of stillbirths and mummified fetuses, and outbreaks are often accompanied by elevated sow mortality. Notably, the Rosalia strain of PRRSV was detected in Spain between December 2020 and October 2021[ 79 ]. During this period, its impact on production and farm economics was severe, characterized by substantial sow losses, persistently high abortion rates of up to 27% over 17 consecutive weeks, and extremely high piglet mortality in nurseries, with peaks ranging from 28% to 50% [ 79 ]. Also, from the seasonality of Fig. 3 , the mortality peaks started to show regularly in winter after 2022. This may be attributed to the higher viability of the virus in cold conditions [ 80 – 82 ]. However, a study conducted in the Midwestern USA reported that, rather than winter, interactions between PRRSV epidemic status and the fall or spring seasons were associated with significantly higher overall sow mortality rates [ 83 ]. Considering the differences in study locations, winter temperatures in our study remained above 0°C, whereas in the Midwest USA they were generally below freezing. Therefore, the winter conditions in our study more closely resemble the spring and fall seasons in the Midwest. As shown in the study of Lugo etc., without covered by snow or soil, PRRSV was shown to persist longer at 10°C compared to − 2°C on surfaces such as plastic, metal, Styrofoam, and cardboard [ 82 ]. These findings suggest that milder winter conditions may actually facilitate PRRSV transmission, which could explain the observed winter mortality peaks in our study. Beyond infectious agents, several physiological and environmental stressors also contribute to stillbirths and sow mortality. Risk factors associated with stillbirths, such as higher parity, larger litter size, and prolonged farrowing duration, contribute to increased reproductive stress, which in turn elevates the risk of sow mortality [ 84 – 88 ]. Also, heat stress not only increases the likelihood of stillbirths but is also recognized as a major contributor to sow mortality [ 86 ]. The impact of body condition score is another critical factor linked to both stillbirth rate[ 84 , 86 ] and mortality risk [ 10 , 89 , 90 ]. In addition, management-related factors such as restrictive farrowing crates, poor locomotion, feed refusal, and inappropriate timing of crate entry increase stillbirth risk [ 91 ]. Therefore, the elevated number of stillbirths observed in deceased sows is unlikely to be an isolated phenomenon. Rather, it may signal a broader syndrome of both stress-related and infectious factors, underlining the need for integrated monitoring of both sow health and reproductive performance to reduce perinatal losses and improve sow longevity. In both reproductive and lifetime performance, dead sows tended to have a younger age at first service. While previous studies have suggested that an earlier first farrowing is linked to increased longevity [ 92 , 93 ]. Survival analysis of reproductive failures showed that a later age at first farrowing significantly heightened the risk of culling [ 94 ]. While this finding seems to contradict our results, it is important to note that our study specifically examined sow mortality, without accounting for culling events or the overall length of sows' productive life on the farm. The average parity of dead sows is 3.42, significantly lower than the average parity at removal for non-mortality sows (shown in Table 8 ). This finding aligns with previous research, which reported an average parity at death ranging from 3.4 to 4.3 [ 11 , 95 ]. Notably, sows must remain in the breeding herd for at least 3 to 4 parities to offset their initial production costs [ 96 , 97 ]. Therefore, reducing sow mortality and extending sow longevity are critical for maintaining economic sustainability and herd productivity. A comparison of the average number of piglets born per parity over a sow’s lifetime between deceased sows and their matched controls revealed that sows that died had larger litters and more live-born piglets on average, and they also exhibited higher numbers of stillbirths and mummified fetuses, along with fewer weaned piglets. The increased litter size and live-born count suggest that deceased sows had superior reproductive performance, which may be linked to their younger age at first service—a factor associated with improved reproductive outcomes in multiple studies [ 16 , 27 , 98 , 99 ]. However, the elevated stillbirth and mummified fetus rates, along with the lower number of weaned piglets, contradict this interpretation. Instead, these findings likely indicate that an increase in stillbirths and mummified fetuses serves as a warning sign of impending mortality. This is particularly evident when examining the final parity before death, where the surge in stillbirth and mummified fetus numbers significantly raised the lifetime average (as shown in Table 7 , where the case group had higher stillbirth and mummified fetus averages than the control group in Table 8 ). It might be speculated that infectious pressures, such as PRRSV outbreaks, could underline part of this association, since PRRSV is known to increase both sow mortality and the incidence of stillbirths and mummified fetuses. However, as no diagnostic information was available in our dataset, this explanation cannot be confirmed and should be considered with caution. Our study has several limitations. While we have access to a large database, the quality of data recording is inconsistent, with numerous extreme values. For instance, some records show unusually long intervals after insemination without a subsequent farrowing record, and we also lack weaning records, preventing us from using weaning as a reference point in our analysis. Additionally, the cause of death records is not sufficiently detailed, as post-mortem examinations were not performed. Furthermore, the absence of comprehensive farm management data limits our ability to assess certain risk factors. Despite these constraints, this study still provides valuable insights for industry professionals. Conclusions This study highlights that the time around farrowing is the most critical risk period for sow mortality, particularly during late gestation and the immediate postpartum phase. These findings emphasize the need for enhanced peripartum monitoring and care. Mortality patterns were shaped by both management strategies and individual sow characteristics. Internal gilt replacement was associated with higher mortality, suggesting that self-replacement herds may require additional health and acclimation protocols. The observed U-shaped relationship with parity reinforces the importance of balanced culling strategies that avoid overuse of both young and old sows. Seasonal effects, especially increased mortality in summer farrowings, point to the importance of environmental management. Interestingly, repeat breeding prior to farrowing was linked to lower mortality, possibly reflecting selective retention of more robust animals. Matched case-control findings suggest that sows with poorer lifetime outcomes may show early-life reproductive disadvantages despite higher fetal outputs, highlighting a disconnect between quantity of production and sow sustainability. These insights underscore the importance of integrating reproductive history into culling decisions and mortality risk assessments. Future research should explore physiological and management factors underlying these associations to inform targeted interventions. Abbreviations Pigs weaned per sow per year (PWSY) Intraclass correlation coefficients (ICC) Confidence interval (CI) Declarations Ethics approval and consent to participate This study did not involve any experimental animals; all data was obtained from production records. Therefore, no ethical approval or informed consent was required. Consent for publication The data used in this study was authorized from Vall Companys. Availability of data and materials The datasets used and/or analyzed during the current study belong to the private company. They are not publicly available. Competing interests The authors declare that they have no competing interest. Funding This study was funded by China Scholarship Council (CSC). Authors' contributions JH contributed to data curation, formal analysis, and preparation of the original draft. CV was responsible for study conceptualization, supervision, and manuscript review and editing. OF, LP, and LC provided methodological support including statistical analysis, validation, and contributed to manuscript review and editing. AM and JM provided essential resources, contributed to data curation, and performed validation. Acknowledgements Thanks for the information and help provided by Antonio Martinez Gilaberte and Jose Murillo from Vall Companys. Without them the study wouldn’t go on smoothly. We also would like to thank Vall Companys for the support to our research. References Kikuti M, Preis GM, Deen J, Pinilla JC, Corzo CA. Sow mortality in a pig production system in the midwestern USA: Reasons for removal and factors associated with increased mortality. Vet Rec. 2023;192:e2539. https://doi.org/10.1002/vetr.2539 . Neila-Ibáñez C, Napp S, Pailler-García L, Franco-Martínez L, Cerón JJ, Aragon V, et al. Risk factors associated with Streptococcus suis cases on pig farms in Spain. Vet Rec. 2023;193:e3056. https://doi.org/10.1002/vetr.3056 . 2021 Sow mortality analysis [Internet]. [cited 2024 Feb 1]. https://www.nationalhogfarmer.com/hog-health/2021-sow-mortality-analysis . Accessed 1 Feb 2024. Deen J, Xue J. Sow mortality in the US: An industry-wide perspective. 1999. Sasaki Y, McTaggart I, Koketsu Y. Assessment of Lifetime Economic Returns of Sows by Parity of Culled Sows in Commercial Breeding Herds. J Vet Epidemol. 2011;16:37–45. https://doi.org/10.2743/jve.16.37 . Levis D. Biological and economical evaluation of sow longevity. Manipulating Pig Production X’(Ed JE Paterson) pp. 2005;171–9. Bergman P, Gröhn YT, Rajala-Schultz P, Virtala A-M, Oliviero C, Peltoniemi O, et al. Sow removal in commercial herds: Patterns and animal level factors in Finland. Prev Vet Med. 2018;159:30–9. https://doi.org/10.1016/j.prevetmed.2018.08.010 . Palomo A. Analysis of sow mortality among breeding sows in Spanish pig herds. 2006 [cited 2024 Feb 2]. http://conservancy.umn.edu/handle/11299/157276 . Accessed 2 Feb 2024. Lagoda ME, Marchewka J, O’Driscoll K, Boyle LA. Risk Factors for Chronic Stress in Sows Housed in Groups, and Associated Risks of Prenatal Stress in Their Offspring. Front Vet Sci. 2022;9:883154. https://doi.org/10.3389/fvets.2022.883154 . Monteiro MS, Matias DN, Poor AP, Dutra MC, Moreno LZ, Parra BM, et al. Causes of Sow Mortality and Risks to Post-Mortem Findings in a Brazilian Intensive Swine Production System. Anim Multidisciplinary Digit Publishing Inst. 2022;12:1804. https://doi.org/10.3390/ani12141804 . Stein TE, Dijkhuizen A, D’Allaire S, Morris RS. Sow culling and mortality in commercial swine breeding herds. Prev Vet Med. 1990;9:85–94. https://doi.org/10.1016/0167-5877(90)90027-F . Abiven N, Seegers H, Beaudeau F, Laval A, Fourichon C. Risk factors for high sow mortality in French swine herds. Prev Vet Med. 1998;33:109–19. https://doi.org/10.1016/S0167-5877(97)00053-6 . D’Allaire S, Drolet R, Brodeur D. Sow mortality associated with high ambient temperatures. Can Vet J. 1996;37:237–9. Holtkamp D, Kliebenstein J, Neumann E, Zimmerman J, Rotto H, Yoder T et al. Assessment of the economic impact of porcine reproductive and respiratory syndrome virus on United States pork producers. 2013. Osemeke O, Silva GS, Corzo CA, Kikuti M, Vadnais S, Yue X, et al. Economic impact of productivity losses attributable to porcine reproductive and respiratory syndrome virus in United States pork production, 2016–2020. Prev Vet Med. 2025;244:106627. https://doi.org/10.1016/j.prevetmed.2025.106627 . Serenius T, Stalder KJ. Length of productive life of crossbred sows is affected by farm management, leg conformation, sow’s own prolificacy. sow’s origin parity Genet Anim. 2007;1:745–50. https://doi.org/10.1017/S175173110769185X . Sasaki Y, Koketsu Y. Mortality, death interval, survivals, and herd factors for death in gilts and sows in commercial breeding herds. J Anim Sci. 2008;86:3159–65. https://doi.org/10.2527/jas.2008-1047 . Halbur PG, Rothschild MF, Thacker BJ, Meng X-J, Paul PS, Bruna JD. Differences in susceptibility of Duroc, Hampshire, and Meishan pigs to infection with a high virulence strain (VR2385) of porcine reproductive and respiratory syndrome virus (PRRSV). J Anim Breed Genet. 1998;115:181–9. https://doi.org/10.1111/j.1439-0388.1998.tb00341.x . Ait-Ali T, Wilson AD, Westcott DG, Clapperton M, Waterfall M, Mellencamp MA, et al. Innate Immune Responses to Replication of Porcine Reproductive And Respiratory Syndrome Virus in Isolated Swine Alveolar Macrophages. Viral Immunology. Volume 20. Mary Ann Liebert, Inc., publishers;; 2007. pp. 105–18. https://doi.org/10.1089/vim.2006.0078 . Ma J, Chen J, Gan M, Chen L, Zhao Y, Zhu Y, et al. Gut microbiota composition and diversity in different commercial swine breeds in early and finishing growth stages. Anim [Internet]. 2022;12. https://doi.org/10.3390/ani12131607 . Song C, Jiang J, Han X, Yu G, Pang Y. Effect of immunological stress to neuroendocrine and gene expression in different swine breeds. Mol Biol Rep. 2014;41:3569–76. https://doi.org/10.1007/s11033-014-3219-1 . Jolingi P, Mok KS, de Vries Reilingh G, Wever PJM, Cornells RS, Oskam JPH, et al. An evaluation of immune competence in different swine breeds. Veterinary Q Taylor Francis. 1993;15:9–15. https://doi.org/10.1080/01652176.1993.9694360 . Cheng C, Wei H, Yu H, Xu C, Jiang S, Peng J. Metabolic Syndrome During Perinatal Period in Sows and the Link With Gut Microbiota and Metabolites. Front Microbiol. 2018;9:1989. https://doi.org/10.3389/fmicb.2018.01989 . Theil PK, Farmer C, Feyera T, Review. Physiology and nutrition of late gestating and transition sows. J Anim Sci. 2022;100:skac176. https://doi.org/10.1093/jas/skac176 . Iida R, Piñeiro C, Koketsu Y. Removal of sows in Spanish breeding herds due to lameness: Incidence, related factors and reproductive performance of removed sows. Prev Vet Med. 2020;179:105002. https://doi.org/10.1016/j.prevetmed.2020.105002 . Pluym LM, Van Nuffel A, Van Weyenberg S, Maes D. Prevalence of lameness and claw lesions during different stages in thereproductive cycle of sows and the impact on reproduction results. Animal. 2013;7:1174–81. https://doi.org/10.1017/S1751731113000232 . Engblom L, Lundeheim N, Strandberg E, Schneider M, del Dalin P, Andersson A-M. Factors affecting length of productive life in Swedish commercial sows. J Anim Sci. 2008;86:432–41. https://doi.org/10.2527/jas.2007-0310 . Björkman S, Kauffold J, Kaiser MØ. Reproductive health of the sow during puerperium. Mol Reprod Dev. 2023;90:561–79. https://doi.org/10.1002/mrd.23642 . Kemper N. Update on postpartum dysgalactia syndrome in sows. J Anim Sci. 2020;98:S117–25. https://doi.org/10.1093/jas/skaa135 . Schwertz CI, Bianchi RM, Cecco BS, Pavarini SP, Driemeier D. Causes of death of sows in three Brazilian pig farms. Pesq Vet Bras Colégio Brasileiro de Patologia Anim - CBPA. 2021;41:e06857. https://doi.org/10.1590/1678-5150-PVB-6857 . Knauer M, Karriker LA, Baas TJ, Johnson C, Stalder KJ. Accuracy of sow culling classifications reported by lay personnel on commercial swine farms. 2007 [cited 2025 Aug 29]; https://doi.org/10.2460/javma.231.3.433 Koketsu Y, Iida R. Farm data analysis for lifetime performance components of sows and their predictors in breeding herds. Porc Health Manag. 2020;6:24. https://doi.org/10.1186/s40813-020-00163-1 . Koketsu Y, Tani S, Iida R. Factors for improving reproductive performance of sows and herd productivity in commercial breeding herds. Porcine Health Manag. 2017;3:1. https://doi.org/10.1186/s40813-016-0049-7 . Piñeiro C, Morales J, Rodríguez M, Aparicio M, Manzanilla EG, Koketsu Y. Big (pig) data and the internet of the swine things: a new paradigm in the industry. [cited 2025 Oct 9]; https://dx.doi.org/10.1093/af/vfz002 . Accessed 9 Oct 2025. Stalder K, D’Allaire S, Drolet R, Abell C. Longevity in breeding animals. Diseases of swine. Wiley Chichester. 2012;10:50–9. Patterson J, Foxcroft G. Gilt management for fertility and longevity. Anim MDPI. 2019;9:434. Sanz-Fernández S, Rodríguez-Hernández P, Díaz-Gaona C, Tusell L, Quintanilla R, Rodríguez-Estévez V. Evolution of Sow Productivity and Evaluation Parameters: Spanish Farms as a Benchmark. Vet Sci. 2024;11:626. https://doi.org/10.3390/vetsci11120626 . Tani S, Piñeiro C, Koketsu Y. Mortality and Survival Probability of Female Pigs in Southern European Commercial Breeding Herds. JAS. 2017;9:38. https://doi.org/10.5539/jas.v9n4p38 . Posit team. RStudio: Integrated development environment for R [Internet]. Boston, MA: Posit Software, PBC; 2025. http://www.posit.co/ . Trenberth KE. What are the seasons? Bull - Am Meteorological Soc. 1983;64:1276–82. Dohoo IR, Martin W, Stryhn HE. Veterinary epidemiologic research [Internet]. 2009 [cited 2023 Nov 3]. https://islandscholar.ca/islandora/object/ir:ir-batch6-2657 . Accessed 3 Nov 2023. Hoving LL, Soede NM, Graat EAM, Feitsma H, Kemp B. Reproductive performance of second parity sows: Relations with subsequent reproduction. Livest Sci. 2011;140:124–30. https://doi.org/10.1016/j.livsci.2011.02.019 . Sasaki Y, Koketsu Y. Variability and repeatability in gestation length related to litter performance in female pigs on commercial farms. Theriogenology. 2007;68:123–7. https://doi.org/10.1016/j.theriogenology.2007.04.021 . Bloemhof S, Mathur PK, Knol EF, van der Waaij EH. Effect of daily environmental temperature on farrowing rate and total born in dam line sows1. J Anim Sci. 2013;91:2667–79. https://doi.org/10.2527/jas.2012-5902 . Iida R, Piñeiro C, Koketsu Y. Incidences and risk factors for prolapse removal in Spanish sow herds. Prev Vet Med. 2019;163:79–86. https://doi.org/10.1016/j.prevetmed.2018.12.016 . Iwagami M, Shinozaki T. Introduction to Matching in Case-Control and Cohort Studies. Ann Clin Epidemiol. 2022;4:33–40. https://doi.org/10.37737/ace.22005 . Benchmarking Summaries |. Swine Management Software | PigCHAMP.com [Internet]. [cited 2025 Feb 27]. https://www.pigchamp.com/benchmarking/benchmarking-summaries . Accessed 27 Feb 2025. England DC. Care of the Sow During Farrowing and Lactation. U.S. Department of Agriculture, Office of Governmental and Public Affairs; 1981. Iida R, Koketsu Y. Climatic factors associated with peripartum pig deaths during hot and humid or cold seasons. Prev Vet Med. 2014;115:166–72. https://doi.org/10.1016/j.prevetmed.2014.03.019 . Bonckaert C, Brossé C, Vandersmissen T, Caliskan N, Buys E, Chantziaras I, et al. Field Study on Sow Mortality in 15 Belgian Pig Farms. Veterinary Sciences. Multidisciplinary Digit Publishing Inst. 2025;12:603. https://doi.org/10.3390/vetsci12070603 . Drolet R, D’Allaire S, Chagnon M. Some observations on cardiac failure in sows. Can Vet J. 1992;33:325–9. Fraser AF. Studies on heat stress in pigs in a tropical environment. Trop Anim Health Prod. 1970;2:76–86. https://doi.org/10.1007/BF02359573 . Zimmerman JJ, editor. Diseases of swine. 10th ed. Chichester, West Sussex: Wiley-Blackwell; 2012. D’Allaire S, Drolet R. Longevity in breeding animals. Diseases of swine. Blackwell PublishingAmes. 2006;9:1011–25. Drolet R. [cited 2024 Jan 31]. Urinary System. Wiley, Ltd;; 2019. pp. 408–24. https://doi.org/10.1002/9781119350927.ch22 . Diseases of Swine [Internet]. Ko Y-R, Lee S-R, Kim S-H, Chae H-D. Pelvic Organ Prolapse Is Associated with Osteoporosis in Korean Women: Analysis of the Health Insurance Review and Assessment Service National Patient Sample. Journal of Clinical Medicine. Volume 10. Multidisciplinary Digital Publishing Institute; 2021. p. 3751. https://doi.org/10.3390/jcm10163751 . Ji M-X, Yu Q. Primary osteoporosis in postmenopausal women. Chronic Diseases and Translational Medicine. Chin Med Association Publishing House. 2015;01:9–13. https://doi.org/10.1016/j.cdtm.2015.02.006 . Ross JW. Identification of putative factors contributing to pelvic organ prolapse in sows [Internet]. 2019 July. https://porkcheckoff.org/research/identification-putative-factors-contributing-pelvic-organ-prolapse-sows/ Koketsu Y. Retrospective analysis of trends and production factors associated with sow mortality on swine-breeding farms in USA. Prev Vet Med. 2000;46:249–56. https://doi.org/10.1016/S0167-5877(00)00153-7 . Zhao Y, Liu X, Mo D, Chen Q, Chen Y. Analysis of reasons for sow culling and seasonal effects on reproductive disorders in Southern China. Anim Reprod Sci. 2015;159:191–7. https://doi.org/10.1016/j.anireprosci.2015.06.018 . Engblom L, Lundeheim N, Dalin A-M, Andersson K. Sow removal in Swedish commercial herds. Livest Sci. 2007;106:76–86. https://doi.org/10.1016/j.livsci.2006.07.002 . Tantasuparuk W, Lundeheim N, Dalin A-M, Kunavongkrit A, Einarsson S. Weaning-to-service interval in primiparous sows and its relationship with longevity and piglet production. Livest Prod Sci. 2001;69:155–62. https://doi.org/10.1016/S0301-6226(00)00256-6 . Tummaruk P, Tantasuparuk W, Techakumphu M, Kunavongkrit A. Influence of repeat-service and weaning-to-first-service interval on farrowing proportion of gilts and sows. Prev Vet Med. 2010;96:194–200. https://doi.org/10.1016/j.prevetmed.2010.06.003 . Liu F, Zhao W, Le HH, Cottrell JJ, Green MP, Leury BJ et al. Review: What have we learned about the effects of heat stress on the pig industry? animal. 2022;16:100349. https://doi.org/10.1016/j.animal.2021.100349 Christensen G, Vraa-Andersen L, Mousing J. Causes of mortality among sows in Danish pig herds. Vet Rec. 1995;137:395–9. https://doi.org/10.1136/vr.137.16.395 . St-Pierre NR, Cobanov B, Schnitkey G. Economic Losses from Heat Stress by US Livestock Industries1. J Dairy Sci. 2003;86:E52–77. https://doi.org/10.3168/jds.S0022-0302(03)74040-5 . Neumann E, Hall W, Stevenson M, Morris R, Ling Min Than J. Descriptive and temporal analysis of post-mortem lesions recorded in slaughtered pigs in New Zealand from 2000 to 2010. New Z Veterinary J Taylor Francis. 2014;62:110–6. https://doi.org/10.1080/00480169.2013.853278 . Sun Y, Jia G, Xu X. Extreme high temperatures and heatwave events across Europe in 2023. Environ Res Commun IOP Publishing. 2025;7:021001. https://doi.org/10.1088/2515-7620/adae60 . Redazione L. Climate change Europe: record-breaking heat and drought in 2024 [Internet]. Rinnovabili. 2025 [cited 2025 Sept 1]. https://www.rinnovabili.net/environment/climate-change/climate-change-europe-record-breaking-heat-and-drought-in-2024/ . Accessed 1 Sept 2025. Rouges E, Ferranti L, Kantz H, Pappenberger F. European heatwaves: Link to large-scale circulation patterns and intraseasonal drivers. Int J Climatol. 2023;43:3189–209. https://doi.org/10.1002/joc.8024 . Spain climate. average weather, temperature, rain - Climates to Travel [Internet]. [cited 2025 Mar 19]. https://www.climatestotravel.com/climate/spain . Accessed 19 Mar 2025. Agencia Estatal de Meteorología (España). Área de Climatología y Aplicaciones Operativas. Resumen estacional climatológico. Verano 2022. Agencia Estatal de Meteorología; 2022 [cited 2025 Sept 1]; https://repositorio.aemet.es/handle/20.500.11765/13966 . Accessed 1 Sept 2025. Agencia Estatal de Meteorología (España). Área de Climatología y Aplicaciones Operativas. Resumen estacional climatológico. Verano 2023. Agencia Estatal de Meteorología; 2023 [cited 2025 Sept 1]; https://repositorio.aemet.es/handle/20.500.11765/15181 . Accessed 1 Sept 2025. Agencia Estatal de Meteorología (España). Área de Climatología y Aplicaciones Operativas. Resumen estacional climatológico. Verano 2024. Agencia Estatal de Meteorología; 2024 [cited 2025 Sept 1]; https://repositorio.aemet.es/handle/20.500.11765/16266 . Accessed 1 Sept 2025. Reporter TI. Sept. Spain’s Third-Hottest Year: A Snapshot of Climate Change in 2024 [Internet]. The International Reporter- Spanish news in English. 2025 [cited 2025 Sept 1]. https://theinternationalreporter.com/spain-weather-hottest-year-dana-climate-change/ . Accessed 1 2025. Unprecedented warmth. A look at Spain’s exceptional summer of 2022. Atmospheric Res Elsevier. 2023;293:106931. https://doi.org/10.1016/j.atmosres.2023.106931 . Exceptional heatwaves and mortality in Europe. Greater impacts since the coronavirus disease 2019 outbreak. Environ Pollution Elsevier. 2024;363:125058. https://doi.org/10.1016/j.envpol.2024.125058 . Cooke AS, Rivero MJ. Livestock heat stress risk in response to the extreme heat event (heatwave) of July 2022 in the UK [Internet]. bioRxiv; 2023 [cited 2025 Sept 1]. p. 2023.05.18.541284. https://doi.org/10.1101/2023.05.18.541284 Martín-Valls GE, Cortey M, Allepuz A, Illas F, Tello M, Mateu E. Introduction of a PRRSV-1 strain of increased virulence in a pig production structure in Spain: virus evolution and impact on production. Porc Health Manag. 2023;9:1. https://doi.org/10.1186/s40813-022-00298-3 . Dee S, Deen J, Rossow K, Wiese C, Otake S, Joo HS, et al. Mechanical transmission of porcine reproductive and respiratory syndrome virus throughout a coordinated sequence of events during cold weather. Can J Vet Res. 2002;66:232–9. Dee S, Deen J, Rossow K, Weise C, Eliason R, Otake S, et al. Mechanical transmission of porcine reproductive and respiratory syndrome virus throughout a coordinated sequence of events during warm weather. Can J Vet Res. 2003;67:12–9. Lugo Mesa V, Quinonez Munoz A, Sobhy NM, Corzo CA, Goyal SM. Survival of Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) in the Environment. Veterinary Sciences. Multidisciplinary Digit Publishing Inst. 2024;11:22. https://doi.org/10.3390/vetsci11010022 . Paiva RC, Moura CA, Thomas P, Haberl B, Greiner L, Rademacher CJ, et al. Risk factors associated with sow mortality in breeding herds under one production system in the Midwestern United States. Prev Vet Med. 2023;213:105883. https://doi.org/10.1016/j.prevetmed.2023.105883 . Vanderhaeghe C, Dewulf J, De Vliegher S, Papadopoulos GA, de Kruif A, Maes D. Longitudinal field study to assess sow level risk factors associated with stillborn piglets. Anim Reprod Sci. 2010;120:78–83. https://doi.org/10.1016/j.anireprosci.2010.02.010 . Sens Junior VL, Rosa ER, Fagundes DP, Peripolli V, Ulguim R da, Siqueira R et al. BP,. Risk factors associated with stillbirth in sows. Cienc Rural. Universidade Federal de Santa Maria; 2023;53:e20220080. https://doi.org/10.1590/0103-8478cr20220080 Rangstrup-Christensen L, Krogh MA, Pedersen LJ, Sørensen JT. Sow-level risk factors for stillbirth of piglets in organic sow herds. animal. 2017;11:1078–83. https://doi.org/10.1017/S1751731116002408 . Nam NH, Sukon P. Risk Factors Associated with Stillbirth in Swine Farms in Vietnam. World’s Veterinary Journal. Volume 10. Турция, Эрзурум: Scienceline Publication Ltd.; 2020. pp. 74–9. Borges VF, Bernardi ML, Bortolozzo FP, Wentz I. Risk factors for stillbirth and foetal mummification in four Brazilian swine herds. Prev Vet Med. 2005;70:165–76. https://doi.org/10.1016/j.prevetmed.2005.03.003 . Sanz M. What we see with sow mortality. 2002 [cited 2025 Mar 19]; https://www.academia.edu/58150623/What_we_see_with_sow_mortality . Accessed 19 Mar 2025. Muro BB, Carnevale RF, Leal DF, Almond GW, Monteiro MS, Poor AP, et al. The importance of optimal body condition to maximise reproductive health and perinatal outcomes in pigs. Nutr Res Rev. 2023;36:351–71. https://doi.org/10.1017/S0954422422000129 . Vargovic L, Athorn RZ, Hermesch S, Bunter KL. Improving sow welfare and outcomes in the farrowing house by identifying early indicators from pre-farrowing assessment. J Anim Sci. 2022;100:skac294. https://doi.org/10.1093/jas/skac294 . Bergman P, Gröhn YT, Rajala-Schultz P, Virtala A-M, Oliviero C, Peltoniemi O, et al. Sow removal in commercial herds: Patterns and animal level factors in Finland. Prev Vet Med. 2018;159:30–9. https://doi.org/10.1016/j.prevetmed.2018.08.010 . Hoge MD, Bates RO. Developmental factors that influence sow longevity1. J Anim Sci. 2011;89:1238–45. https://doi.org/10.2527/jas.2010-3175 . Engblom L, Eliasson-Selling L, Lundeheim N, Belák K, Andersson K, Dalin A-M. Post mortem findings in sows and gilts euthanised or found dead in a large Swedish herd. Acta Vet Scand. 2008;50:25. https://doi.org/10.1186/1751-0147-50-25 . Duran CO. Sow Mortality. Food Anim. 2001;23:S76–83. Stalder KJ, Lacy RC, Cross TL, Conatser GE, Darroch CS. Net Present Value Analysis of Sow Longevity and the Economic Sensitivity of Net Present Value to Changes in Production, Market Price, Feed Cost, and Replacement Gilt Costs in a Farrow-to-Finish Operation. Prof Anim Sci. 2000;16:33–40. https://doi.org/10.15232/S1080-7446(15)31658-2 . Stalder K, Lacy C, Cross T, Conatser G. Financial impact of average parity of culled females in a breed-to-wean swine operation using replacement gilt net present value analysis. JSHAP. 2003;11:69–74. https://doi.org/10.54846/jshap/357 . Stalder K, Knauer M, Baas T, Rothschild M, Mabry J. Sow Longevity. Pig News Inform. 2004;25:53–74. Patterson JL, Beltranena E, Foxcroft GR. The effect of gilt age at first estrus and breeding on third estrus on sow body weight changes and long-term reproductive performance. J Anim Sci. 2010;88:2500–13. https://doi.org/10.2527/jas.2008-1756 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8865197","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597224252,"identity":"631366c3-8f27-4d4b-9055-2eccfb55ded5","order_by":0,"name":"Junhao Huang","email":"","orcid":"","institution":"IRTA-UAB Joint Research Unit in Animal Health. Centre for Research in Animal Health (CReSA). Campus of the Autonomous University of Barcelona (UAB","correspondingAuthor":false,"prefix":"","firstName":"Junhao","middleName":"","lastName":"Huang","suffix":""},{"id":597224260,"identity":"5c1225a7-a303-45e8-9cc9-8fafd2030415","order_by":1,"name":"Osvaldo Fonseca","email":"","orcid":"","institution":"IRTA-UAB Joint Research Unit in Animal Health. Centre for Research in Animal Health (CReSA). Campus of the Autonomous University of Barcelona (UAB","correspondingAuthor":false,"prefix":"","firstName":"Osvaldo","middleName":"","lastName":"Fonseca","suffix":""},{"id":597224264,"identity":"9a99fba4-4791-47b5-ab56-a9012f544032","order_by":2,"name":"Lola Pailler","email":"","orcid":"","institution":"IRTA-UAB Joint Research Unit in Animal Health. Centre for Research in Animal Health (CReSA). Campus of the Autonomous University of Barcelona (UAB","correspondingAuthor":false,"prefix":"","firstName":"Lola","middleName":"","lastName":"Pailler","suffix":""},{"id":597224275,"identity":"d0c11faf-aaad-4576-89de-d886901b547b","order_by":3,"name":"Lorena Correa","email":"","orcid":"","institution":"IRTA-UAB Joint Research Unit in Animal Health. Centre for Research in Animal Health (CReSA). Campus of the Autonomous University of Barcelona (UAB","correspondingAuthor":false,"prefix":"","firstName":"Lorena","middleName":"","lastName":"Correa","suffix":""},{"id":597224276,"identity":"502d5fa7-a1f8-4b0e-9401-87885148973f","order_by":4,"name":"Antonio Martínez","email":"","orcid":"","institution":"Vall Companys","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Martínez","suffix":""},{"id":597224278,"identity":"8c3c114c-e101-4402-a5f0-86f297b76cee","order_by":5,"name":"Jose Murillo","email":"","orcid":"","institution":"Vall Companys","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"","lastName":"Murillo","suffix":""},{"id":597224280,"identity":"f08d4e03-76a3-4f84-bf18-485acfa602df","order_by":6,"name":"Carles Vilalta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie3Pv4vCMBTA8VcepEuOrhlE/4WAwy2if4sIdXFwdHA4OEiXszc7+Tf4H7xSqIs/VsElk3Mn6RDkUuWGG9I63pDvEJKQD48A+Hz/s4DqlYWPzYC/ZJ4E7YYg5sAel7KdwJPk0Eqi5KCpNDBOEXVeLk+d982nhmppnETspzJbKxgrZJKouPBOwWTwVbinSIghf/uAvv2LJezCBQOJwBpIdIXcmJqEJdH9aElYItwbiLBT7I+7DLmkTJElXGKg3EScr5CtlKjJnA7pxJLZPFulfSeJvuNAV2bAe0my1YvbcCRwt9XVreskv8P+Hqntvc/n8/ma+wEKYk6g+J4hGgAAAABJRU5ErkJggg==","orcid":"","institution":"IRTA-UAB Joint Research Unit in Animal Health. Centre for Research in Animal Health (CReSA). Campus of the Autonomous University of Barcelona (UAB","correspondingAuthor":true,"prefix":"","firstName":"Carles","middleName":"","lastName":"Vilalta","suffix":""}],"badges":[],"createdAt":"2026-02-12 19:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8865197/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8865197/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103864245,"identity":"4a3fbe36-3ce6-42c3-adc6-8c1237a03527","added_by":"auto","created_at":"2026-03-03 21:25:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56883,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of annual mortality rates across 16 farms from 2019 - 2024. The values at the top, middle, and bottom of each box indicate the 75th percentile, median (50th percentile), and 25th percentile, respectively.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8865197/v1/d642ea59d8b5e48b4ab09c4b.png"},{"id":104400976,"identity":"bf7716f0-4960-4fe2-bfa0-989888bbed49","added_by":"auto","created_at":"2026-03-11 12:11:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79078,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative annual distribution of the top 10 causes of death (2019–2024). Each colored area corresponds to a cause, showing its proportion in total deaths over the years. Colors distinguish causes.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8865197/v1/0bd7b7299799c50cc9f3c8a6.png"},{"id":103864249,"identity":"b908288d-9258-4f02-9add-a2871ff208cc","added_by":"auto","created_at":"2026-03-03 21:25:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52540,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of calculated sow weekly mortality (blue line) and its 95% CI (grey area) during the period of study (January 1\u003csup\u003est\u003c/sup\u003e, 2019, to December 31\u003csup\u003est\u003c/sup\u003e, 2024). Seasonal periods were shown as shaded and labeled background bands for comparison (winter: Win, spring: Spr, summer: Sum, autumn: Aut).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8865197/v1/1758a09aef479371a86fbb34.png"},{"id":103864247,"identity":"98ca134f-f306-4f59-b652-8de2ea3a5220","added_by":"auto","created_at":"2026-03-03 21:25:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":90219,"visible":true,"origin":"","legend":"\u003cp\u003eRelative mortality percentages from the total number of dead animals occurring at different times after the last recorded service. Blue: died before farrowing and therefore those animals do not have a registered farrowing event. Red: Died after a registered farrowing event. Bins in both categories coincide have narrower bars.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8865197/v1/94b6ab92a714851cae2cbde3.png"},{"id":103864250,"identity":"f720f3c6-7631-431e-b148-8df496461873","added_by":"auto","created_at":"2026-03-03 21:25:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":125247,"visible":true,"origin":"","legend":"\u003cp\u003eWeekly mortality incidence rate (cases per 1000 sow-years) of pigs in each month of the pregnant period (red = month 0, green = month 1, blue = month 2, orange = month 3) with fitted trend lines and 95% CI. Colored dots represent weekly mortality incidence rate, calculated with death count of the week divided by the sow-years at risk of the week, while solid lines show smoothed trends. Seasonal periods were shown as shaded and labeled background bands for comparison (winter: Win, spring: Spr, summer: Sum, autumn: Aut). Grey-shaded areas represent 95% confidence intervals for the fitted trends.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8865197/v1/56dacdfcbc8bc960d1d3d9f8.png"},{"id":103864248,"identity":"276e75ff-8b7a-4c33-8a73-68b15531df06","added_by":"auto","created_at":"2026-03-03 21:25:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":69136,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of death causes distribution of the critical periods of sow production cycle. Time period: A: 105-111 days after service, B: 112-118 days after service, C: 0-6 days after delivery, D: 21-27 days after delivery, E:7 weeks after delivery. Only causes that account for more than 0.5% over the whole period are displayed, and within each year, only causes exceeding 0.5% are labeled. Different causes were in different colors and labeled with their portions in percentage.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8865197/v1/71f03b77ac3fb700285f194c.png"},{"id":107388248,"identity":"f0e416e6-e58a-431d-8f3d-e3d76d45f8f7","added_by":"auto","created_at":"2026-04-21 04:24:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1608859,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8865197/v1/b0df7b1d-a052-4ced-ab3d-a37c5e9d82c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sow mortality risk factors at different reproductive stages: an analysis of production data","fulltext":[{"header":"Background","content":"\u003cp\u003eThe increasing mortality rate of breeding sows has been rising at an alarming rate in recent years, with significant implications for various aspects of farm operations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In herds across the USA, Canada, Australia, and the Philippines, sow mortality reached 13.56% in 2021, following a steady rise from 7.32% to 11.78% between 2012 and 2018 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This upward trend in sow mortality has resulted in productivity losses, particularly as a large proportion of these deaths occur during gestation, leading to significant piglet losses per sow per year.\u003c/p\u003e \u003cp\u003eThe financial burden associated with sow mortality extends beyond direct losses, encompassing replacement costs, reduced reproductive output, and potential disruptions in herd management. Replacing a deceased sow can be costly, especially when gilt availability is limited, sometimes necessitating the retention of sows that would otherwise be culled. Additionally, gestational losses further amplify economic setbacks, with total expenses per lost sow estimated at \u003cspan\u003e$\u003c/span\u003e400\u0026ndash;\u003cspan\u003e$\u003c/span\u003e500 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A sow reaches positive net income around the third parity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and profit per litter rises with each subsequent parity by \u003cspan\u003e$\u003c/span\u003e100\u0026ndash;\u003cspan\u003e$\u003c/span\u003e200 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Thus, early loss or culling leads to considerable economic loss, with studies estimating an accumulated profit gap of \u003cspan\u003e$\u003c/span\u003e360\u0026ndash;\u003cspan\u003e$\u003c/span\u003e441 between sows producing three and six litters.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHerd-level risk factors for sow mortality are influenced by a range of biological and environmental elements. Key factors include farm-specific conditions like housing, nutrition, and health management [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Feeding practices influence sow mortality by affecting digestive and metabolic stress [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. High ambient temperature and humidity markedly increase sow mortality by overwhelming heat-dissipation mechanisms, leading to cardiovascular failure, especially during poorly ventilated, humid summer periods without nighttime cooling or acclimatization [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Biosecurity management is a key determinant of infectious disease occurrence. Among swine diseases, porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most critical, raising the death rate by approximately 14% compared with na\u0026iuml;ve or stable breeding herds [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], causing an estimated annual economic loss of \u003cspan\u003e$\u003c/span\u003e380.82\u0026nbsp;million in the U.S. breeding sector from 2016 to 2020 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSow-level factors include sow characteristics such as reproductive performance, age at first farrowing, and parity, along with genetic breed [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Sows with a younger age at first farrowing and larger first-litter size were found to have longer lifespans [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In high-parity sows, mortality is elevated from torsions of abdominal organs, prolapse, and cystitis-pyelonephritis, while gilts and first-parity sows are vulnerable due to physiological immaturity, pelvic constraints, and stress associated with first estrus, mating, and parturition [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Breed affects sow mortality through susceptibility to PRRSV infection [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], viral replication in alveolar macrophages [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], gut microbiota composition [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and immune response capacity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSows undergo marked physiological and metabolic changes across reproductive stages, particularly between gestation and the post-farrowing period [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. During late gestation and early lactation, sows experience substantial shifts in energy balance, endocrine regulation, immune function, and physical load [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Previous studies have reported different distributions of diseases across reproductive stages. For example, lameness was reported of showing differing prevalence or incidence patterns across gestation and lactation [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Similar stage-dependent patterns have also been observed for other disorders, including reproductive complications, metabolic diseases, and infectious conditions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. As established contributors to sow mortality, these conditions may contribute to the previously reported variation in the frequency and distribution of sow deaths across reproductive stages.\u003c/p\u003e \u003cp\u003eConsistent with these physiological differences, sow mortality shows stage-dependent variation. However, most existing studies have focused on counts or proportions of sow deaths, rather than on incidence rates that account for the duration of exposure within each reproductive stage [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The contribution of reproductive stages to sow mortality risk has not been fully quantified. Similarly, the influence of sow-level and herd-level factors on mortality across reproductive stages, when assessed using incidence rates rather than absolute counts, has not been systematically investigated. Filling this gap could provide more precise identification of sows at higher risk of death and inform targeted management strategies.\u003c/p\u003e \u003cp\u003eIn commercial pig production, data management and recording systems are essential for monitoring productivity and health. However, many farms still rely on manual record-keeping, resulting in inconsistent or incomplete data. Studies have shown wide variation in data quality across farms depending on herd size, management software, and personnel training [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, in most cases, these records are mainly used for routine purposes, such as generating sow cards, task schedules, or brief performance overviews, rather than being analyzed to extract meaningful insights [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. When systematically analyzed, herd data can provide a powerful foundation for evidence-based management, enabling producers and veterinarians to enhance sow lifetime reproductive potential and overall production efficiency [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Spain is one of the most important swine production countries in the world, with almost 35,000,00 pigs produced in 2023 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, studies over sow mortality are rare in this region [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], particularly using large-scale data.\u003c/p\u003e \u003cp\u003eTherefore, given the observed stage-dependent variation in sow mortality, the limited availability of large-scale production data, and the scarcity of studies in Spain, this study aims to analyze sow mortality trends across reproductive stages in 16 Spanish swine farms over a six-year period and to identify key factors contributing to elevated mortality rates and individual sow mortality risk using production records.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eDataset preparation\u003c/p\u003e \u003cp\u003eProduction data from a Spanish swine production company were used in this study. These records were collected daily as part of the farms\u0026rsquo; routine management procedures. The database included production data from 16 breeding herds conveniently selected by the producer. These farms were chosen based on the company's prior experience, as they provided more detailed and consistent classification of sow mortality compared to the rest of the farms in the system. Data management, descriptive statistics and analyses were performed using R studio [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDefinitions and metrics\u003c/p\u003e \u003cp\u003eIn this study, a sow is defined as a female pig that had been inseminated at least once. Parity was defined as the number of farrowing completed, meaning that parity increased only after successful farrowing. A service is defined as any insemination event occurring during estrus and could include one or multiple mating events within the same estrus cycle.\u003c/p\u003e \u003cp\u003eThe period of study is comprised between January 2019 and December 2024. Gilts that were culled before their first service were excluded from the analysis, since not all farms consistently recorded such removals. The dataset used for this study was composed of five files from the production record system with sow individual records. Variables used from each file are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the files and fields used to create the data frames analyzed in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFields\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFARM CENSUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSow ID, Entering Date, Parity At Farrowing, Farrowing Date, Elimination Date, Elimination Type (Death, Euthanasia or Culling), Cause of Elimination, Breed, Non-Productive Days At Elimination.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLITTERS BORN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSow ID, Parity, Farrowing Date, Total Born Piglets, Live Born Piglets, Dead Born Piglets, Mummies, Adopted Weaned, Weaned Piglets, Breed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBREEDING INFORMATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreeding Date, Sow ID, Parity, Breeding Number, Previous Breeding Date.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEWLY BREED GILTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreeding Date, Result, Type, Sow ID, Expected Farrowing Date, Discharge Date, Age at First Breeding.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSows can be eliminated by culling, death or euthanasia. The death of a sow was also categorized as accident, illness, peripartum, prolapse, sudden death, ulcer, locomotor problems, evicted or clostridium. This latter categorization is done by farm personnel following the company guidelines:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAccident: deaths resulting from trauma or physical injury, such as crushing, falls, or entrapment within housing facilities.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIllness: deaths caused by clinically diagnosed diseases not falling into other specific categories, including systemic infections or chronic conditions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePeripartum: deaths occurring around the time of farrowing, often associated with complications of parturition or immediate postpartum physiological stress.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProlapse: deaths resulting from vaginal, uterine, or rectal prolapse, typically occurring during late gestation or shortly after farrowing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSudden death: deaths without prior clinical signs, characterized by abrupt onset and rapid progression.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUlcer: deaths associated with gastric or gastric-proximal intestinal ulceration leading to hemorrhage or perforation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLocomotor problems: deaths attributable to lameness, fractures, arthritis, or other musculoskeletal disorders impairing mobility.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eClostridium: deaths with compatible clinical signs of infection with clostridium species (e.g. bloated).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSeasons were defined according to conventional meteorological periods [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]: Winter (from December 1st to February 28/29th ), Spring (from March 1st to May 31st ), Summer (from June 1st to August 31st ) and Fall (from September 1st to November 30th )\u003c/p\u003e \u003cp\u003eThe risk of death of sows was calculated as the number of dead sows divided by the number of removed sows, multiplied by 100.\u003c/p\u003e \u003cp\u003eAnnualized mortality incidence rates (expressed as cases per 1000 sow-years) were calculated by dividing the number of sow deaths by the total sow-years at risk and multiplying the result by 1000 sows [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Sow-years at risk were calculated from the date of first service to the date of removal. For sows that were still alive at the time of data extraction, sow-years at risk were defined as the time between the first service and the last recorded event (e.g. service or farrowing).\u003c/p\u003e \u003cp\u003eBased on established criteria from previous studies, certain records were identified as extreme values and treated as missing data (the value was replaced by NA and not taken account in the analysis). These included: gilt age at first service below 160 days or above 400 days (11,008 records; [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]); gestation lengths shorter than 105 days or longer than 125 days (73 records; [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]); and total piglets born fewer than 1 or greater than 30 (173 records; [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]). Additionally, other extreme values were excluded, such as: cases where piglet deaths exceeded the total number of piglets born plus those fostered to the sow (1,486 records); weaned piglet counts for sows nursing piglets from other dams (37,594 records); piglets weaned numbering fewer than 1 or exceeding 30 (14,931 records); and nonproductive sow days of 366 days or more (94 records).\u003c/p\u003e \u003cp\u003eDescriptive analysis\u003c/p\u003e \u003cp\u003eFor the overall weekly mortality analysis, death counts and average weekly sow population were aggregated by week, and weekly mortality was calculated as the ratio of deaths to population. Shaded and labeled background bands were used to represent winter, spring, summer, and autumn. Temporal trends were smoothed using a LOESS function, and individual weekly mortality points were plotted. X-axis labels were formatted by date, with major breaks every six months and minor breaks every month. This visualization allowed examination of both weekly mortality fluctuations and seasonal patterns over the study period.\u003c/p\u003e \u003cp\u003eWeekly sow mortality and pregnancy-week mortality were analyzed using recorded sow deaths and sow population data from 2019 to 2024. For the pregnancy-week analysis, each sow was assigned a pregnancy month based on the time elapsed from service, and weekly death counts and sow-years at risk were aggregated by week and pregnancy month. Mortality incidence per week was calculated as the number of deaths divided by the sow-years at risk.\u003c/p\u003e \u003cp\u003eIncidence rate analysis\u003c/p\u003e \u003cp\u003eUsing the GLIMMIX procedure, two-level Poisson regression models were applied:\u003c/p\u003e \u003cp\u003e \u003cb\u003e1. To estimate the incidence rate of death while accounting for herd variability and to analyse its associations with herd-level factors.\u003c/b\u003e \u003c/p\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe herd-level factors that we examined were herd size, piglets weaned per sow per year (PWSY), and whether the farm bred their own gilts. Herd size was calculated as the average annual inventory of sows in the farm for each year during the study period. For each sow, the number of days spent on the farm within the given year was calculated based on entry and removal dates. These days were summed across all sows in the farm and divided by 365.25 to convert the total into sow-years, representing the average herd size for that year. PWSY was annually calculated from 2019 to 2024 and then were averaged for each farm in six 1-year periods.\u003c/p\u003e \u003cp\u003eTo achieve this first objective, an intercept-only Poisson models with an offset and no fixed or random effects were applied to estimate simple incidence rates, and random effects were added later to estimate overall and herd-level incidence rates, accounting for herd effects, along with their 95% confidence intervals (CI). and random effects were added later to estimate overall and herd-level incidence rates with their 95% confidence intervals (CI). From this model, the random herd effect with its standard error was also obtained. Additionally, intraclass correlation coefficients (ICC) were calculated as the proportion of variance explained by herd-level differences, using the R package \u0026ldquo;iccCount\u0026rdquo;. In this study, herd-level management factors were included in the analysis, with gilt replacement strategy (own vs. external) modeled as a fixed effect, and numerical indicators such as herd size and pigs weaned per sow per year (PWSY) evaluated using Pearson\u0026rsquo;s correlations with herd-level incidence rates.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\u003cp\u003e \u003cb\u003e2. To examine the associations between the incidence rate of death and parity-level factors.\u003c/b\u003e \u003c/p\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAssessed parity-level factors included weeks from service or delivery, parity at service or delivery, re-service, service or delivery season. To address the second objective, parity-level factors were added to the models as fixed effects in a univariate manner. For analyses of time from service or farrowing, individual records were expanded into week-level risk sets, covering the period from each reproductive event (service or farrowing) until the next event or sow death. Weekly mortality outcomes were recorded, and sow-years at risk were calculated for each interval to account for the time at risk in the models [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eSince farrowing is a major risk factor for sow mortality, the model was applied separately to two distinct time periods: the period from insemination to farrowing and the period from farrowing to the next insemination. The number of deaths and the logarithm of sow-years at risk divided by 1000 were set as the outcome variable and the offset, respectively, to predict the number of deaths per 1000 sow-years.\u003c/p\u003e \u003cp\u003eMatched case-control study\u003c/p\u003e \u003cp\u003eThree matched studies were performed to investigate differences between dead sows (cases) and their matched controls, defined as sows that left the herd for reasons other than death or that remained alive:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAge at first service was compared between gilts that died before their first parity and their matched controls (gilts that did give birth to the first parity). Controls were matched on parity, farm, number of services, year of service, and season.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLitter reproductive performance was compared between case parity records (last parities before sow death) and matched control parity records from sows that did not die after the corresponding parity. Matching was based on parity number, farm, farrowing year, and season. Variables included gestation length, total piglets born, stillborn piglets, mummified fetuses, and piglets weaned. This analysis aimed to identify potential pre-death signals in litter performance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLifetime performance metrics were evaluated by comparing deceased sows with their matched controls (those alive or removed for reasons other than death) to assess differences in long-term productivity. The average litter performance was determined by averaging the sow's lifetime totals across the parity. The controls were matched with the case based on the farm, first service year and season.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe R package \u0026ldquo;ccoptimalmatch\u0026rdquo; was used in this study to match each case with up to four controls. Although not all cases could be matched with four controls, the majority were successfully matched at this 1:4 ratio, which has been shown to maximize cost-effectiveness and statistical efficiency [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe dataset from the 16 herds comprised 488,849 service records from 106,351 sows. Out of 106,351 sows, 76,607 sows were removed during the study period. The removal reasons were culling (71.6%; 54,848 sows), dead (21.7%; 16,634 sows), euthanasia (6.4%; 4,877 sows), and others (0.3%; 248 sows).\u003c/p\u003e \u003cp\u003eThe average annual mortality rate for the 16 farms was 5.20% (95%CI : 4.45% \u0026minus;\u0026thinsp;5.95%), 7.06% (95%CI : 6.11% \u0026minus;\u0026thinsp;8.00%), 6.80% (95%CI : 5.76% \u0026minus;\u0026thinsp;7.83%), 8.15% (95%CI : 6.58% \u0026minus;\u0026thinsp;9.71%), 9.82% (95%CI : 7.73% \u0026minus;\u0026thinsp;11.91%), 9.74% (95%CI : 7.42% \u0026minus;\u0026thinsp;12.06%) from 2019 to 2024, respectively. A summary of each farm yearly mortality can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe overall causes of mortality (dead) during the studied period were accident (5.42%; 901 cases), clostridium (3.75%; 624 cases), evicted (8.73%; 1453 cases), illness (9.70%; 1613 cases), locomotor problems (0.73%; 122 cases), peripartum (10.45%; 1738 cases), prolapse (10.00%; 1664 cases), sudden death (45.17%; 7514), ulcer (2.71%; 451 cases), or others (2.94%; 489 cases). The distribution of death causes from each year is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, weekly mortality rates exhibited an overall upward trend, especially after 2022, with noticeable seasonal peaks during the summer and winter months.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe descriptive statistics for the different factors used for the farm- and sow-level analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The average herd size and PWSY during the period 2019\u0026ndash;2024 were 1998.89 sows and 23.73, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of herd and sow-level factors used for the analysis in the 16 selected farms. SD: Standard deviation, IQR: Interquartile Range.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHerd level data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHerd size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1998.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2005.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1288.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(379.68\u0026thinsp;\u0026minus;\u0026thinsp;2197.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e452.74\u0026thinsp;\u0026minus;\u0026thinsp;8749.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(22.51\u0026thinsp;\u0026minus;\u0026thinsp;24.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e21.28\u0026thinsp;\u0026minus;\u0026thinsp;29.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSow-level data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity at removal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(1.5\u0026thinsp;\u0026minus;\u0026thinsp;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonproductive sow days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(-6.5\u0026thinsp;\u0026minus;\u0026thinsp;58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first service, days old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e275.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(245.5\u0026thinsp;\u0026minus;\u0026thinsp;294.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e160\u0026thinsp;\u0026minus;\u0026thinsp;401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal piglets born alive per sow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(21.5\u0026thinsp;\u0026minus;\u0026thinsp;80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal piglets weaned per sow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(19\u0026thinsp;\u0026minus;\u0026thinsp;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays from last delivery to death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(3.5\u0026thinsp;\u0026minus;\u0026thinsp;22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays from last service to death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(58.5\u0026thinsp;\u0026minus;\u0026thinsp;125.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity-level data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of parities at service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(0.5\u0026thinsp;\u0026minus;\u0026thinsp;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(114.5\u0026thinsp;\u0026minus;\u0026thinsp;115.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e105\u0026thinsp;\u0026minus;\u0026thinsp;125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePiglets born alive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e344745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(12\u0026thinsp;\u0026minus;\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePiglets born still\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e344745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(0\u0026thinsp;\u0026minus;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePiglets born mummified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e344745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(-0.5\u0026thinsp;\u0026minus;\u0026thinsp;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePiglets weaned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e291485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(61.25\u0026thinsp;\u0026minus;\u0026thinsp;85.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e3.57\u0026thinsp;\u0026minus;\u0026thinsp;2400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eService-level data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e291635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(11\u0026thinsp;\u0026minus;\u0026thinsp;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e439788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(1\u0026thinsp;\u0026minus;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e details the risk and proportion of death removals across parities. 15.64% of records (16,634 out of 106,351) indicated removal due to death. The risk of death was highest in the parity 6\u0026thinsp;+\u0026thinsp;group (5.9%), followed by parity 1 (3.1%). Gilts were the ones with the lowest death risk (1.6%). It increased to 3.1% at parity 1 and then steadily decreased with increasing parity, remaining relatively low through parity 5 (2.4%). However, a marked increase was observed in the parity 6\u0026thinsp;+\u0026thinsp;group, where the risk rose sharply to 5.9%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk and proportion of death by parity at removal.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of dead sows\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSows at risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRemoved sows\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRisk of removal due to death, %\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProportion of death, %\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Denominator was the number of sows at risk.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e2\u003c/sup\u003e Denominator was the number of removed sows.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the total sow deaths (16,634), 44.41% (7,387) occurred after farrowing, while 55.59% (9,247) happened before farrowing (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The weeks with the highest mortality rates were as follows: 105\u0026ndash;111 days of gestation, accounting for 6.93% (1,153/16,634) of deaths; 112\u0026ndash;118 days, the peak period, with 21.57% (3,588/16,634); 119\u0026ndash;125 days at 11.91% (1,981/16,634); 126\u0026ndash;132 days at 6.89% (1,136/16,634); and 133\u0026ndash;139 days at 8.63% (1,435/16,634).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe highest mortality incidence was observed in pigs during their last month of pregnancy, with a pronounced peak occurring specifically in the summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The contrast between the last month of pregnancy and the other months, as well as the difference between summer and the other seasons, became more evident after 2022.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe overall sow mortality incidence rate across the 16 farms was 99.26 deaths per 1,000 sow-years (95% CI: 97.84-100.71), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The simple estimated mortality rate for serviced sows was 67.44 (95% CI: 66.12\u0026ndash;68.78), while farrowed sows had a much higher rate of 242.5 (95% CI: 237.7-247.36) deaths per 1,000 sow-years. When considering median incidence rates across herds, serviced animals had a median of 62.866 (95% CI: 56.04\u0026ndash;70.520), whereas farrowed sows had a median of 193.831 (95% CI: 158.748-236.668) deaths per 1,000 sow-years (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ICC for these rates were found to range from 99.85% to 99.93%, indicating high herd-level variability. To further explore herd-level factors, a fixed factor was included in the model to assess the effect of self-breeding of gilts. Results showed that farms that bred their own gilts had a significantly higher mortality rate in farrowed sows (p-value 0.0140) and observation in total duration (p-value 0.0126). Additionally, Pearson\u0026rsquo;s correlation analysis, presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, herd size and PWSY did not show a significant association with mortality rates at any stage.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence rate of death in serviced and farrowed sows in 16 farms.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeasurements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrom service until farrowing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrom farrowing until subsequent service\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal duration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of sows at risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSow-years at risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137112.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30461.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167574.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncidence rate cases per 1000 sow-years (95% CI)\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimple estimate (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.44 (66.12, 68.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e242.5 (237.7, 247.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.26 (97.84, 100.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimate taking herd effect into account (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.866 (56.043, 70.520)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193.83 (158.75, 236.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.795 (77.11, 99.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel intercept (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.141 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.267 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.475 (0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom herd effect (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0515 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1603 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0681 (0.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC, %\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence rate if the farms grow their own gilts (p-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.48 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e299.87(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117.06 (0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePearson\u0026rsquo;s correlation coefficient with herd management measurements (p-value)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHerd size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36 (0.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24 (0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37 (0.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e The Simple estimate reflects the sow-level incidence, while the herd-adjusted estimate represents the median rate across herds.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e2\u003c/sup\u003e The sow-years at risk was set to 1000 sow-years in the ICC calculation. ICC: intraclass correlation coefficients\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDeath incidence in serviced sows was associated with the weeks from the last service, number of parities, number of services and service season (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). There is a 3.69-fold increase at 14\u0026ndash;15 weeks (101.085 cases per 1,000 sow-years), a 20.65-fold increase at 16\u0026ndash;17 weeks (565.35 cases per 1000 sow-years) and a 8.66-fold increase at 18 weeks and more, compared to the baseline incidence observed at 0\u0026ndash;1 weeks (27.372 cases per 1000 sow-years). The mortality incidence rate initially decreases with increasing parity but then rises again. Individuals at parity 2 exhibited the lowest mortality incidence. The highest rates are observed at parity 5 (71.969 cases per 1,000 sow-years) and parity 6 or higher (68.516 cases per 1,000 sow-years), where they reach 1.22 and 1.16 times the incidence rate of parity 0 (59.049 cases per 1,000 sow-years), respectively. Sows that underwent repeated services had a pre-farrowing mortality incidence rate (55.81 cases per 1,000 sow-years) that was 0.87 times that of sows serviced only once (63.95 cases per 1,000 sow-years). The mortality incidence rate for sows serviced in the spring was the highest compared to other seasons, at 73.66 cases per 1,000 sow-years.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated incidence rate (cases per 1,000 sow-years) for death of serviced sows prior to farrowing, using the cohort data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of records\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal sow-years at risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncidence rate (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137112.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.87 (56.04, 70.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeeks from service, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1 (0\u0026ndash;13 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18658.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.37 (23.89, 31.36)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3 (14\u0026ndash;27 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18166.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.21 (38.80, 48.13)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;5 (28\u0026ndash;41 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e484671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17082.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.44 (36.20, 45.17)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;7 (42\u0026ndash;55 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e476567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16454.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.32 (38.81, 48.35)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u0026ndash;9 (56\u0026ndash;69 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e470119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16061.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.47 (32.51, 40.91)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;11 (70\u0026ndash;83 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e465355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15770.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.94 (34.76, 43.63)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;13 (84\u0026ndash;97 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e461607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15523.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.80 (45.61, 56.58)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u0026ndash;15 (98\u0026ndash;111 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15263.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107.53 (97.68, 118.37)\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;17 (112\u0026ndash;125 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e454331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3826.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e565.35 (515.00, 620.63)\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18 or more (over 126 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e444153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e304.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e544.33 (459.50, 644.81)\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of parities at the service, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26722.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.05 (52.19, 66.81)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23220.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.31 (52.46, 60.44)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19796.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.19 (55.02, 63.67)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16953.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.41 (60.74, 70.46)\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14368.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.43 (61.46, 71.80)\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11950.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.97 (66.41, 77.99)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24099.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.52 (64.10, 73.24)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Services, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e396687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118800.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.95 (56.93, 71.83)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRe-service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18312.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.81 (48.9, 63.7)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eService season, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35454.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.66 (64.53, 84.07)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34860.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.44 (50.25, 65.67)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33967.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.26 (56.1, 71.34)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32829.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.67 (49.55, 64.82)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCI: confidence interval.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u0026minus;f\u003c/sup\u003e Estimates within a group with different letters are different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e1\u003c/sup\u003e The variables were analyzed univariately in the model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, similar to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, presents the associations between fixed factors and mortality incidence in farrowed sows, specifically examining the effects of weeks from delivery, parity number, service times and season. The incidence of sow mortality was highest during the first week after farrowing (week 0), reaching 278.72 cases per 1,000 sow-years. This rate declined in the subsequent weeks but began to rise again after week 2. Notably, from week 7 onward, the mortality incidence exceeded that of the immediate post-farrowing period, with weekly rates of 393.80, 323.90, 354.53, and 455.41 cases per 1,000 sow-years recorded during weeks 7 to 10 and beyond. Except for parity 1 (212.27 cases per 1,000 sow-years), mortality incidence in all other parities remained below the overall incidence rate (193.83 cases per 1,000 sow-years). The difference in mortality incidence between first-service and re-serviced sows is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating a higher mortality rate in re-serviced sows compared to those first-serviced. Seasonal effects were evident, with the highest mortality incidence recorded in sows that farrowed during the summer (225.41 cases per 1,000 sow-years).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated incidence rate (cases per 1,000 sow-years) for dead sows after farrowing without subsequent service, using the cohort data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of records\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal sow-years at risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncidence rate (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30461.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193.83 (158.75, 236.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeeks from delivery, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 (0\u0026ndash;6 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7238.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e278.72 (228.37, 340.18)\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 (7\u0026ndash;13 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e485225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7160.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147.32 (119.41, 181.76)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 (14\u0026ndash;20 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e482622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7066.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127.23 (103.01, 157.16)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 (21\u0026ndash;27 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e479111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5326.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182.22 (147.61, 224.95)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 (28\u0026ndash;34 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e471135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1797.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206.06 (164.83, 257.61)\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 (35\u0026ndash;41 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e735.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e263.92 (207.75, 335.29)\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 (42\u0026ndash;48 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e425.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e240.53 (184, 314.44)\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 (49\u0026ndash;55 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e451975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e393.8 (298.59, 519.36)\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 (56\u0026ndash;62 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e448573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e323.9 (232.91, 450.43)\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 (63\u0026ndash;69 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e445457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e354.53 (248.34, 506.13)\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10 or higher (over 70 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e442646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e455.41 (351.14, 590.63)\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of parities at delivery, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6406.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e212.27 (173.3, 260)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5110.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e192.16 (154.88, 238.4)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4346.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193.5 (155.76, 240.37)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3673.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182.17 (146.32, 226.8)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3105.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187.53 (150.38, 233.84)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7819.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187.85 (151.78, 232.5)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Services, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e396687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27994.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e190 (155.77, 231.77)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRe-service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2466.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e237.54 (192.05, 293.81)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDelivery season, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7355.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180.03 (145.32, 223.04)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8326.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e225.41 (182.25, 278.8)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8017.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e178.51 (145.64, 218.79)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6761.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187.97 (151.7, 232.92)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCI: confidence interval.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u0026minus;f\u003c/sup\u003e Estimates within a group with different letters are different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e1\u003c/sup\u003e The variables were analyzed univariately in the model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the high-risk periods for sow mortality identified in this study, the causes of death were categorized and are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Among sows that died between 105\u0026ndash;111 days after service, sudden death was the most prevalent cause, followed by prolapse and peripartum. During 112\u0026ndash;118 days after service, the most common cause became peripartum, followed by sudden death and prolapse. In the 0\u0026ndash;6 days post-farrowing period, peripartum complications were still the leading cause, followed by prolapse, sudden death. For sows that died 21\u0026ndash;27 days post-farrowing, sudden death was the most frequent cause, followed by eviction and accident. Lastly, 7 weeks after delivery, the distribution shifted toward more external or environmental causes, such as accidents, eviction, while sudden death still contributed to a measurable fraction of deaths.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the comparisons of nine reproductive performance measurements between deceased sows and their matched controls. For the sows that died before giving birth for the first time, they had a significantly younger age at first service (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Among sows that died post-farrowing, their final parity was characterized by a shorter gestation length (p\u0026thinsp;=\u0026thinsp;0.014), fewer total piglets born (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), fewer live-born piglets (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and a higher incidence of stillbirths (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Although the overall number of piglets weaned did not differ significantly between groups (p\u0026thinsp;=\u0026thinsp;0.337), a significant difference was observed in the number of mummified fetuses (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, stratified analyses revealed contrasting results: the number of mummified piglets was not significantly different (p\u0026thinsp;=\u0026thinsp;0.807), while the number of piglets weaned showed a significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons of reproductive performance between case records and matched control records in matched case-control study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eReproductive performance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGilt age at first service, days old\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271.238 (35.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2646\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e278.102 (33.618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000 (0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational length, days\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.398 (0.877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29496\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115.457 (0.877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal piglets born\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.036 (4.704)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26304\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.848 (2.498)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets born alive\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.447 (4.991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26304\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.858 (2.364)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets born still\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.000 (2.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26304\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.424 (1.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets born mummified\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.589 (1.371)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26304\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.567 (0.727)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000 (0.807)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets weaned\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.024 (3.440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8230\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.008 (1.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.337 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Cases (the first service records of sows that received the first service but died before they could give birth) and controls (the first service records of sows that didn\u0026rsquo;t die before their first parity) were matched by parity, farm, number of services received, service year and season.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Cases (the last litter records of sows that died after the parity) and controls (the litter records of sows that didn\u0026rsquo;t die after the parity) were matched by parity, farm, number of services received, service year and season.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e3\u003c/sup\u003e 610 cases have 1 control, 216 cases have 2 controls, 108 cases have 3 controls, and 320 cases have 4 controls.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e4\u003c/sup\u003e 4 cases have 1 control, 4 cases have 2 controls, 12 cases have 3 controls, and 7362 cases have 4 controls.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e5\u003c/sup\u003e 4 cases have 1 control, 4 cases have 2 controls, 12 cases have 3 controls, and 6564 cases have 4 controls.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e6\u003c/sup\u003e 4 cases have 1 control, 2 cases have 2 controls, 2 cases have 3 controls, and 2054 cases have 4 controls.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e7\u003c/sup\u003e p-values for the stratified analyses are shown in the parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, deceased sows showed distinct lifetime performance patterns compared to their matched controls (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), including higher average numbers of total and live-born piglets per parity, but also increased stillbirths and mummified fetuses, and fewer piglets weaned. Additionally, deceased sows had a younger age at first service and fewer nonproductive days. Stratified analyses further supported these associations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of lifetime performance between case sows and matched control sows\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLifetime performance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGilt age at first service, days old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272.488\u0026nbsp;(35.589)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59098\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e274.136\u0026nbsp;(26.897)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u0026nbsp;(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity at removal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.420\u0026nbsp;(2.582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60635\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.043\u0026nbsp;(2.170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u0026nbsp;(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage number of piglets born per parity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.398\u0026nbsp;(4.407)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47219\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.836\u0026nbsp;(4.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u0026nbsp;(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage number of piglets born alive per parity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.640\u0026nbsp;(4.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47219\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.192\u0026nbsp;(3.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u0026nbsp;(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage number of piglets born still per parity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.245\u0026nbsp;(1.471)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47219\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.162\u0026nbsp;(0.721)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u0026nbsp;(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage number of piglets born mummified per parity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.514\u0026nbsp;(0.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47219\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.482\u0026nbsp;(0.470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u0026nbsp;(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage number of piglets weaned per parity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.611\u0026nbsp;(3.595)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41323\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.659\u0026nbsp;(3.406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u0026nbsp;(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonproductive sow days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.707\u0026nbsp;(54.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47774\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.119\u0026nbsp;(39.332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u0026nbsp;(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Case sows were sows that died\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Control sows were alive or removed for non-mortality reasons. The controls were matched with the case based on the farm, first service year and season.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e3\u003c/sup\u003e 73 cases have 1 control, 1,034 cases have 2 controls, 3,587 cases have 3 controls, and 11,549 cases have 4 controls.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e4\u003c/sup\u003e 68 cases have 1 control, 1,036 cases have 2 controls, 3,625 cases have 3 controls, and 11,905 cases have 4 controls\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e5\u003c/sup\u003e 10 cases have 1 control, 783 cases have 2 controls, 2,957 cases have 3 controls, and 9,193 cases have 4 controls.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e6\u003c/sup\u003e 352 cases have 2 controls, 2,441 cases have 3 controls, and 8,324 cases have 4 controls.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e7\u003c/sup\u003e 2,640 cases have 1 control, 3,789 cases have 2 controls, 2,544 cases have 3 controls, and 7,481 cases have 4 controls.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e8\u003c/sup\u003e p-values for the stratified analyses are shown in the parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, sow mortality was observed to increase generally during the 6-year period from 2019 to 2024 in Spain. This increasing trend is a broader global pattern. For instance, in the United States, the sow mortality rate has also shown a steady rise over recent years, with reported rates of 12.31% in 2019, 13.91% in 2020, 14.86% in 2021, 14.54% in 2022, and 14.675% in 2023[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Notably, the mortality rates observed in this study were lower than those reported in the U.S. during the same period. This is consistent with benchmarking studies suggesting that Spain generally exhibits better swine production performance compared to the U.S.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eRegarding the timing of sow deaths, mortality incidence was higher post-farrowing than during gestation. Peak rates observed between 105\u0026ndash;118 days of gestation (near farrowing), and in the first weeks after farrowing, aligning with findings from previous studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These findings suggest that late gestation and farrowing are the most critical and high-risk event for sow mortality, emphasizing the need for intensive monitoring of pregnant sows during late gestation. Typically, sows are recommended to be moved to the farrowing site no later than day 110 of gestation [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]; however, based on these results, an earlier relocation at day 105 may be more appropriate. Notably, a sharp increase in mortality incidence was observed after 4 weeks post-farrowing, a pattern also reported in studies of lameness and prolapse incidence [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, based on standard production cycles, most sows are weaned during the third week post-farrowing and typically return to enter estrus for the next breeding cycle. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the sow-years at risk dropped markedly after the fourth week, indicating that the majority of sows had already been bred and exited the post-farrowing phase. Therefore, sows remaining at risk beyond week 4 likely represent a subset with reproductive issues, illness, or management decisions against further breeding. This suggests that the elevated mortality after week 4 is not due to post-weaning timing itself but rather reflects underlying conditions in a vulnerable subpopulation. In other words, the increased mortality is likely a consequence of health or reproductive failure, not a risk factor inherent to the post-weaning period. In the period 7 weeks after delivery, the incidence of death appears elevated. However, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the leading causes of death in this time window, aside from sudden death, were accidents, evictions, and illness, suggesting a different risk profile compared to earlier periods. Notably, prolapse is rare, while causes linked to systemic illness or external events dominate. This pattern supports the interpretation that sows remaining at risk beyond week 4 are likely to represent a selected subset with reproductive failure, health issues, or were intentionally removed from the breeding herd. Thus, the high mortality rate is not inherently due to the post-weaning timing but rather reflects the underlying vulnerability of this subgroup.\u003c/p\u003e \u003cp\u003eThe causes of death during these critical periods remain poorly documented in our dataset. This is a common phenomenon across the swine industry. For example, in a study evaluating the accuracy of sow culling classifications, 23% (209/923) of farm-reported culling codes were found to be inaccurate [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In our result, sudden death predominated across most time points, likely due to the lack of post-mortem examinations, which are often cost-prohibitive. Although the category \"peripartum\" refers to deaths caused by dystocia\u0026mdash;a condition of difficult or prolonged farrowing\u0026mdash;it may clinically manifest as sudden death prior to piglet expulsion, particularly during unsupervised nighttime farrowing. In such cases, without direct observation, it is nearly impossible for farm personnel to distinguish between death due to dystocia and sudden death, leading to potential misclassification in recorded data. Similarly, \"illness\" is a broad classification, indicating that farmers noticed abnormalities in the sow before death and may have even attempted treatment, but without a definitive diagnosis. The term \"eviction\" is also ambiguous and does not clearly indicate a cause of death. Among the primary causes of sow mortality, prolapse stands out as the most well-defined and least likely to be misdiagnosed or overlooked. This degree of inaccuracy was believed that it may cause severe limitations for studies that rely on farm-reported assessments of clinical conditions. Given these limitations, farmers should receive better training to improve their ability to monitor and document sow health conditions. Keeping more detailed records before sudden deaths occur could provide valuable supplementary information for diagnosis. Additionally, farmers should be equipped with basic skills to conduct preliminary assessments, helping to identify potential health risks earlier.\u003c/p\u003e \u003cp\u003eIn other studies, the peripartum period was described to have more than 50% of sow deaths [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and the possible reasons are heart failures, genitourinary lesions, and prolapses based on post-mortem diagnosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Necropsy-based investigations have further revealed that mortality in sows often involves diverse underlying conditions. For example, one study reported positional changes in internal organs (32% of 100 deaths), arthritis (19%), and urogenital disorders (7%) as the most frequent findings in dead sows [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Several stressors, such as heat stress, aggressive interactions, and increased body weight, have been identified as contributing factors to peripartum mortality, particularly due to heart failure [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In addition to cardiovascular issues, urinary tract infections are a leading global cause of culling and sudden death in sows [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Heavier pigs and those with lameness issues have a higher likelihood of ascending infections because their lower body parts are more likely to come into contact with surfaces contaminated by feces [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], while sows in late gestation due to their increased weight have more likelihood to urinary tract infections. Floor types that have better drainage effects should be considered. Both gestation and farrowing are significant risk factors for prolapse, as the reproductive process weakens connective tissues, making prolapse a major concern. Studies have identified parity, litter size, and piglet birth weight as contributing factors, with pelvic organ misalignment resulting from structural weakening [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In humans, pelvic organ prolapse has been linked to low bone mineral density [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], and postmenopausal women are at higher risk due to estrogen deficiency. Similarly, among late-gestation sows, those with lower body condition scores have shown a higher incidence of prolapse compared to those in normal or overweight conditions[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Given these findings, optimizing prenatal nutrition, particularly calcium supplementation, may help reduce the risk of prolapse.\u003c/p\u003e \u003cp\u003eIn our study, the weaning period (typically the third week post-farrowing) exhibited a noticeable peak in the number of sow deaths, with sudden death being the most frequently recorded cause. However, when adjusted for sow-years at risk, the mortality incidence during this period was not exceptionally elevated. It is important to note that \"sudden death\" is a broad classification that lacks specificity regarding the underlying pathological mechanisms. To gain deeper insight, we referred to a previous study that reported a 5.35% mortality rate (6 out of 112 sows) during the weaning-to-estrus interval. In that study, confirmed causes of death included heart failure (2 cases), liver lobe torsion (2 cases), and vaginal or rectal prolapse (2 cases) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Notably, two-thirds of these cases could potentially explain some of the sudden deaths observed in our study. However, due to the small sample size of that study, it does not fully capture the range of potential causes during this period, nor does it clarify the true composition of sudden deaths in our dataset. Several factors may contribute to increased mortality at weaning, including the stress of separation from piglets, hormonal fluctuations associated with the return to estrus, and stress induced by relocation. These factors could exacerbate underlying health conditions, ultimately leading to sudden death.\u003c/p\u003e \u003cp\u003eFor the herd-level risk factors, although internal gilt replacement is commonly associated with enhanced biosecurity, our findings indicate that farms employing this strategy had higher sow mortality rates after farrowing and throughout the reproductive cycle. Importantly, this association was not confounded by herd size or productivity (PWSY), as neither factor showed a significant relationship with mortality in our data. This suggests that the elevated risk may stem from other herd-level dynamics. One plausible explanation is that internal replacement systems require careful management of gilt development, and any shortcomings, such as inadequate body condition, improper age at first service, or insufficient acclimatization, may predispose sows to peripartum stress and health issues. Additionally, limited genetic diversity or suboptimal selection within closed herds may allow physically weaker individuals to enter the breeding population, increasing vulnerability during critical reproductive stages. These findings underscore the importance of managing replacement gilt quality, not just quantity, in herds practicing internal replacement.\u003c/p\u003e \u003cp\u003eOur results indicate that sows beyond parity 6 face the highest overall mortality risk, likely due to age-related health deterioration and increased susceptibility to disease. High parity sows had a higher mortality incidence rate after service and a rather lower incidence rate after farrowing than other parities. Meanwhile, parity 1 sows also experienced elevated mortality, primarily due to a higher post-farrowing death rate than in any other parity. This may stem from the physiological immaturity of young sows, as their bodies are still developing and may not yet be fully prepared for pregnancy and farrowing. Mortality incidence declines with parity, reaching its lowest point at parity 4, before rising again in later parities. In contrast, pre-farrowing mortality is lowest in parity 1 sows but increases steadily with advancing parity.\u003c/p\u003e \u003cp\u003eMortality risk varies across studies depending on the parity cycle phase but is generally highest in low-parity sows or those at parity 6 and beyond [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. One possible explanation for this discrepancy is differences in culling strategies across studies. In our study, farms may have lower culling efficiency for older sows, meaning that more high-parity sows remain in the herd until they die, rather than being removed pre-emptively. In contrast, studies with more aggressive culling policies may observe fewer deaths in older sows simply because they are removed before reaching a critical state of health.\u003c/p\u003e \u003cp\u003eService history appears to be associated with differential mortality risk across reproductive stages. Sows that underwent repeat services exhibited a lower mortality rate during the gestation period. One possible explanation is that the additional time between services may allow these sows more opportunities to recover physically before conceiving again. However, previous studies have associated a longer weaning-to-service interval with increased culling rates and reduced farrowing proportions [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. This may be because in those studies, sows with poorer reproductive health either failed to conceive at the first service or were deliberately delayed by farm managers, reflecting underlying health concerns that predisposed them to culling or reproductive inefficiency. Since our study focuses exclusively on mortality (not culling), the lower mortality observed in repeat-service sows may partly reflect a survivorship bias\u0026mdash;sows with evident reproductive issues might have already been removed from the herd before death. Supporting this interpretation, our post-farrowing mortality analysis revealed that sows with repeat services had a higher incidence of death after farrowing, suggesting that reproductive dysfunctions reflected by repeat services may manifest more clearly as health risks in the postpartum period.\u003c/p\u003e \u003cp\u003eSeveral studies have established that high mortality rates in both winter and summer are an industry-wide concern [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan additionalcitationids=\"CR65 CR66\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. In the results, sows bred in spring and those farrowing in summer had the highest mortality rates. Given that pre-farrowing deaths were concentrated in the final one to two weeks of gestation, the mortality peak for spring-bred sows still occurred in summer. This suggests that high summer temperatures were a primary factor driving sow mortality in our study. This seasonal temperature pattern likely explains the significantly higher mortality rates in summer compared to winter. Specially, since 2022, summer heat waves were constantly reported in Europe [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Spain, located in the southern Europe, suffered the most from the global warming among European countries because of its drought in summer [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. According to the State Meteorological Agency (AEMET), 2023, 2022, and 2024 were the first, second and third-hottest years [\u003cspan additionalcitationids=\"CR73 CR74\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].Most of the farms included in the studies were from the north east of Spain, including Catalonia, Aragon and Valencia. This part of Spain experienced the hottest summer in 700 years in 2022 [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. These heat waves have caused numerous human fatalities [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] and, unsurprisingly, also significantly impacted livestock production [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. All this summing up explained and matched the extreme mortality peak after 2022 in the summers we observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAccording to the matched case-control study, sows that died had a higher number of stillbirths. Infectious diseases should be considered in this context. For example, PRRSV is a well-documented cause of stillbirths and mummified fetuses, and outbreaks are often accompanied by elevated sow mortality. Notably, the Rosalia strain of PRRSV was detected in Spain between December 2020 and October 2021[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. During this period, its impact on production and farm economics was severe, characterized by substantial sow losses, persistently high abortion rates of up to 27% over 17 consecutive weeks, and extremely high piglet mortality in nurseries, with peaks ranging from 28% to 50% [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Also, from the seasonality of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the mortality peaks started to show regularly in winter after 2022. This may be attributed to the higher viability of the virus in cold conditions [\u003cspan additionalcitationids=\"CR81\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. However, a study conducted in the Midwestern USA reported that, rather than winter, interactions between PRRSV epidemic status and the fall or spring seasons were associated with significantly higher overall sow mortality rates [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Considering the differences in study locations, winter temperatures in our study remained above 0\u0026deg;C, whereas in the Midwest USA they were generally below freezing. Therefore, the winter conditions in our study more closely resemble the spring and fall seasons in the Midwest. As shown in the study of Lugo etc., without covered by snow or soil, PRRSV was shown to persist longer at 10\u0026deg;C compared to \u0026minus;\u0026thinsp;2\u0026deg;C on surfaces such as plastic, metal, Styrofoam, and cardboard [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. These findings suggest that milder winter conditions may actually facilitate PRRSV transmission, which could explain the observed winter mortality peaks in our study.\u003c/p\u003e \u003cp\u003eBeyond infectious agents, several physiological and environmental stressors also contribute to stillbirths and sow mortality. Risk factors associated with stillbirths, such as higher parity, larger litter size, and prolonged farrowing duration, contribute to increased reproductive stress, which in turn elevates the risk of sow mortality [\u003cspan additionalcitationids=\"CR85 CR86 CR87\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Also, heat stress not only increases the likelihood of stillbirths but is also recognized as a major contributor to sow mortality [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. The impact of body condition score is another critical factor linked to both stillbirth rate[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e] and mortality risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. In addition, management-related factors such as restrictive farrowing crates, poor locomotion, feed refusal, and inappropriate timing of crate entry increase stillbirth risk [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, the elevated number of stillbirths observed in deceased sows is unlikely to be an isolated phenomenon. Rather, it may signal a broader syndrome of both stress-related and infectious factors, underlining the need for integrated monitoring of both sow health and reproductive performance to reduce perinatal losses and improve sow longevity.\u003c/p\u003e \u003cp\u003eIn both reproductive and lifetime performance, dead sows tended to have a younger age at first service. While previous studies have suggested that an earlier first farrowing is linked to increased longevity [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Survival analysis of reproductive failures showed that a later age at first farrowing significantly heightened the risk of culling [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. While this finding seems to contradict our results, it is important to note that our study specifically examined sow mortality, without accounting for culling events or the overall length of sows' productive life on the farm.\u003c/p\u003e \u003cp\u003eThe average parity of dead sows is 3.42, significantly lower than the average parity at removal for non-mortality sows (shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This finding aligns with previous research, which reported an average parity at death ranging from 3.4 to 4.3 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. Notably, sows must remain in the breeding herd for at least 3 to 4 parities to offset their initial production costs [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Therefore, reducing sow mortality and extending sow longevity are critical for maintaining economic sustainability and herd productivity.\u003c/p\u003e \u003cp\u003eA comparison of the average number of piglets born per parity over a sow\u0026rsquo;s lifetime between deceased sows and their matched controls revealed that sows that died had larger litters and more live-born piglets on average, and they also exhibited higher numbers of stillbirths and mummified fetuses, along with fewer weaned piglets. The increased litter size and live-born count suggest that deceased sows had superior reproductive performance, which may be linked to their younger age at first service\u0026mdash;a factor associated with improved reproductive outcomes in multiple studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the elevated stillbirth and mummified fetus rates, along with the lower number of weaned piglets, contradict this interpretation. Instead, these findings likely indicate that an increase in stillbirths and mummified fetuses serves as a warning sign of impending mortality. This is particularly evident when examining the final parity before death, where the surge in stillbirth and mummified fetus numbers significantly raised the lifetime average (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, where the case group had higher stillbirth and mummified fetus averages than the control group in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). It might be speculated that infectious pressures, such as PRRSV outbreaks, could underline part of this association, since PRRSV is known to increase both sow mortality and the incidence of stillbirths and mummified fetuses. However, as no diagnostic information was available in our dataset, this explanation cannot be confirmed and should be considered with caution.\u003c/p\u003e \u003cp\u003eOur study has several limitations. While we have access to a large database, the quality of data recording is inconsistent, with numerous extreme values. For instance, some records show unusually long intervals after insemination without a subsequent farrowing record, and we also lack weaning records, preventing us from using weaning as a reference point in our analysis. Additionally, the cause of death records is not sufficiently detailed, as post-mortem examinations were not performed. Furthermore, the absence of comprehensive farm management data limits our ability to assess certain risk factors. Despite these constraints, this study still provides valuable insights for industry professionals.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights that the time around farrowing is the most critical risk period for sow mortality, particularly during late gestation and the immediate postpartum phase. These findings emphasize the need for enhanced peripartum monitoring and care. Mortality patterns were shaped by both management strategies and individual sow characteristics. Internal gilt replacement was associated with higher mortality, suggesting that self-replacement herds may require additional health and acclimation protocols. The observed U-shaped relationship with parity reinforces the importance of balanced culling strategies that avoid overuse of both young and old sows.\u003c/p\u003e \u003cp\u003eSeasonal effects, especially increased mortality in summer farrowings, point to the importance of environmental management. Interestingly, repeat breeding prior to farrowing was linked to lower mortality, possibly reflecting selective retention of more robust animals.\u003c/p\u003e \u003cp\u003eMatched case-control findings suggest that sows with poorer lifetime outcomes may show early-life reproductive disadvantages despite higher fetal outputs, highlighting a disconnect between quantity of production and sow sustainability. These insights underscore the importance of integrating reproductive history into culling decisions and mortality risk assessments. Future research should explore physiological and management factors underlying these associations to inform targeted interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePigs weaned per sow per year (PWSY)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIntraclass correlation coefficients (ICC)\u003c/p\u003e\n\u003cp\u003eConfidence interval (CI)\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study did not involve any experimental animals; all data was obtained from production records. Therefore, no ethical approval or informed consent was required.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe data used in this study was authorized from Vall Companys.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study belong to the private company. They are not publicly available.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study was funded by China Scholarship Council (CSC).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eJH contributed to data curation, formal analysis, and preparation of the original draft.\u003c/p\u003e\n\u003cp\u003eCV was responsible for study conceptualization, supervision, and manuscript review and editing.\u003c/p\u003e\n\u003cp\u003eOF, LP, and LC provided methodological support including statistical analysis, validation, and contributed to manuscript review and editing.\u003c/p\u003e\n\u003cp\u003eAM and JM provided essential resources, contributed to data curation, and performed validation.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThanks for the information and help provided by Antonio Martinez Gilaberte and Jose Murillo from Vall Companys. Without them the study wouldn\u0026rsquo;t go on smoothly. We also would like to thank Vall Companys for the support to our research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKikuti M, Preis GM, Deen J, Pinilla JC, Corzo CA. Sow mortality in a pig production system in the midwestern USA: Reasons for removal and factors associated with increased mortality. Vet Rec. 2023;192:e2539. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/vetr.2539\u003c/span\u003e\u003cspan address=\"10.1002/vetr.2539\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeila-Ib\u0026aacute;\u0026ntilde;ez C, Napp S, Pailler-Garc\u0026iacute;a L, Franco-Mart\u0026iacute;nez L, Cer\u0026oacute;n JJ, Aragon V, et al. Risk factors associated with Streptococcus suis cases on pig farms in Spain. Vet Rec. 2023;193:e3056. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/vetr.3056\u003c/span\u003e\u003cspan address=\"10.1002/vetr.3056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2021 Sow mortality analysis [Internet]. [cited 2024 Feb 1]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nationalhogfarmer.com/hog-health/2021-sow-mortality-analysis\u003c/span\u003e\u003cspan address=\"https://www.nationalhogfarmer.com/hog-health/2021-sow-mortality-analysis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 1 Feb 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeen J, Xue J. Sow mortality in the US: An industry-wide perspective. 1999.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasaki Y, McTaggart I, Koketsu Y. Assessment of Lifetime Economic Returns of Sows by Parity of Culled Sows in Commercial Breeding Herds. J Vet Epidemol. 2011;16:37\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2743/jve.16.37\u003c/span\u003e\u003cspan address=\"10.2743/jve.16.37\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevis D. Biological and economical evaluation of sow longevity. Manipulating Pig Production X\u0026rsquo;(Ed JE Paterson) pp. 2005;171\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergman P, Gr\u0026ouml;hn YT, Rajala-Schultz P, Virtala A-M, Oliviero C, Peltoniemi O, et al. Sow removal in commercial herds: Patterns and animal level factors in Finland. Prev Vet Med. 2018;159:30\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.prevetmed.2018.08.010\u003c/span\u003e\u003cspan address=\"10.1016/j.prevetmed.2018.08.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalomo A. Analysis of sow mortality among breeding sows in Spanish pig herds. 2006 [cited 2024 Feb 2]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://conservancy.umn.edu/handle/11299/157276\u003c/span\u003e\u003cspan address=\"http://conservancy.umn.edu/handle/11299/157276\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 2 Feb 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLagoda ME, Marchewka J, O\u0026rsquo;Driscoll K, Boyle LA. Risk Factors for Chronic Stress in Sows Housed in Groups, and Associated Risks of Prenatal Stress in Their Offspring. Front Vet Sci. 2022;9:883154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fvets.2022.883154\u003c/span\u003e\u003cspan address=\"10.3389/fvets.2022.883154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro MS, Matias DN, Poor AP, Dutra MC, Moreno LZ, Parra BM, et al. Causes of Sow Mortality and Risks to Post-Mortem Findings in a Brazilian Intensive Swine Production System. Anim Multidisciplinary Digit Publishing Inst. 2022;12:1804. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ani12141804\u003c/span\u003e\u003cspan address=\"10.3390/ani12141804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStein TE, Dijkhuizen A, D\u0026rsquo;Allaire S, Morris RS. Sow culling and mortality in commercial swine breeding herds. Prev Vet Med. 1990;9:85\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0167-5877(90)90027-F\u003c/span\u003e\u003cspan address=\"10.1016/0167-5877(90)90027-F\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbiven N, Seegers H, Beaudeau F, Laval A, Fourichon C. Risk factors for high sow mortality in French swine herds. Prev Vet Med. 1998;33:109\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0167-5877(97)00053-6\u003c/span\u003e\u003cspan address=\"10.1016/S0167-5877(97)00053-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026rsquo;Allaire S, Drolet R, Brodeur D. Sow mortality associated with high ambient temperatures. Can Vet J. 1996;37:237\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoltkamp D, Kliebenstein J, Neumann E, Zimmerman J, Rotto H, Yoder T et al. Assessment of the economic impact of porcine reproductive and respiratory syndrome virus on United States pork producers. 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsemeke O, Silva GS, Corzo CA, Kikuti M, Vadnais S, Yue X, et al. Economic impact of productivity losses attributable to porcine reproductive and respiratory syndrome virus in United States pork production, 2016\u0026ndash;2020. Prev Vet Med. 2025;244:106627. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.prevetmed.2025.106627\u003c/span\u003e\u003cspan address=\"10.1016/j.prevetmed.2025.106627\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerenius T, Stalder KJ. Length of productive life of crossbred sows is affected by farm management, leg conformation, sow\u0026rsquo;s own prolificacy. sow\u0026rsquo;s origin parity Genet Anim. 2007;1:745\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S175173110769185X\u003c/span\u003e\u003cspan address=\"10.1017/S175173110769185X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasaki Y, Koketsu Y. Mortality, death interval, survivals, and herd factors for death in gilts and sows in commercial breeding herds. J Anim Sci. 2008;86:3159\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2527/jas.2008-1047\u003c/span\u003e\u003cspan address=\"10.2527/jas.2008-1047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalbur PG, Rothschild MF, Thacker BJ, Meng X-J, Paul PS, Bruna JD. Differences in susceptibility of Duroc, Hampshire, and Meishan pigs to infection with a high virulence strain (VR2385) of porcine reproductive and respiratory syndrome virus (PRRSV). J Anim Breed Genet. 1998;115:181\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1439-0388.1998.tb00341.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1439-0388.1998.tb00341.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAit-Ali T, Wilson AD, Westcott DG, Clapperton M, Waterfall M, Mellencamp MA, et al. Innate Immune Responses to Replication of Porcine Reproductive And Respiratory Syndrome Virus in Isolated Swine Alveolar Macrophages. Viral Immunology. Volume 20. Mary Ann Liebert, Inc., publishers;; 2007. pp. 105\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/vim.2006.0078\u003c/span\u003e\u003cspan address=\"10.1089/vim.2006.0078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa J, Chen J, Gan M, Chen L, Zhao Y, Zhu Y, et al. Gut microbiota composition and diversity in different commercial swine breeds in early and finishing growth stages. Anim [Internet]. 2022;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ani12131607\u003c/span\u003e\u003cspan address=\"10.3390/ani12131607\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong C, Jiang J, Han X, Yu G, Pang Y. Effect of immunological stress to neuroendocrine and gene expression in different swine breeds. Mol Biol Rep. 2014;41:3569\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11033-014-3219-1\u003c/span\u003e\u003cspan address=\"10.1007/s11033-014-3219-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJolingi P, Mok KS, de Vries Reilingh G, Wever PJM, Cornells RS, Oskam JPH, et al. An evaluation of immune competence in different swine breeds. Veterinary Q Taylor Francis. 1993;15:9\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01652176.1993.9694360\u003c/span\u003e\u003cspan address=\"10.1080/01652176.1993.9694360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng C, Wei H, Yu H, Xu C, Jiang S, Peng J. Metabolic Syndrome During Perinatal Period in Sows and the Link With Gut Microbiota and Metabolites. Front Microbiol. 2018;9:1989. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2018.01989\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2018.01989\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTheil PK, Farmer C, Feyera T, Review. Physiology and nutrition of late gestating and transition sows. J Anim Sci. 2022;100:skac176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jas/skac176\u003c/span\u003e\u003cspan address=\"10.1093/jas/skac176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIida R, Pi\u0026ntilde;eiro C, Koketsu Y. Removal of sows in Spanish breeding herds due to lameness: Incidence, related factors and reproductive performance of removed sows. Prev Vet Med. 2020;179:105002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.prevetmed.2020.105002\u003c/span\u003e\u003cspan address=\"10.1016/j.prevetmed.2020.105002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePluym LM, Van Nuffel A, Van Weyenberg S, Maes D. Prevalence of lameness and claw lesions during different stages in thereproductive cycle of sows and the impact on reproduction results. Animal. 2013;7:1174\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1751731113000232\u003c/span\u003e\u003cspan address=\"10.1017/S1751731113000232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngblom L, Lundeheim N, Strandberg E, Schneider M, del Dalin P, Andersson A-M. Factors affecting length of productive life in Swedish commercial sows. J Anim Sci. 2008;86:432\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2527/jas.2007-0310\u003c/span\u003e\u003cspan address=\"10.2527/jas.2007-0310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBj\u0026ouml;rkman S, Kauffold J, Kaiser M\u0026Oslash;. Reproductive health of the sow during puerperium. Mol Reprod Dev. 2023;90:561\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/mrd.23642\u003c/span\u003e\u003cspan address=\"10.1002/mrd.23642\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKemper N. Update on postpartum dysgalactia syndrome in sows. J Anim Sci. 2020;98:S117\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jas/skaa135\u003c/span\u003e\u003cspan address=\"10.1093/jas/skaa135\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwertz CI, Bianchi RM, Cecco BS, Pavarini SP, Driemeier D. Causes of death of sows in three Brazilian pig farms. Pesq Vet Bras Col\u0026eacute;gio Brasileiro de Patologia Anim - CBPA. 2021;41:e06857. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/1678-5150-PVB-6857\u003c/span\u003e\u003cspan address=\"10.1590/1678-5150-PVB-6857\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnauer M, Karriker LA, Baas TJ, Johnson C, Stalder KJ. Accuracy of sow culling classifications reported by lay personnel on commercial swine farms. 2007 [cited 2025 Aug 29]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2460/javma.231.3.433\u003c/span\u003e\u003cspan address=\"10.2460/javma.231.3.433\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoketsu Y, Iida R. Farm data analysis for lifetime performance components of sows and their predictors in breeding herds. Porc Health Manag. 2020;6:24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40813-020-00163-1\u003c/span\u003e\u003cspan address=\"10.1186/s40813-020-00163-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoketsu Y, Tani S, Iida R. Factors for improving reproductive performance of sows and herd productivity in commercial breeding herds. Porcine Health Manag. 2017;3:1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40813-016-0049-7\u003c/span\u003e\u003cspan address=\"10.1186/s40813-016-0049-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePi\u0026ntilde;eiro C, Morales J, Rodr\u0026iacute;guez M, Aparicio M, Manzanilla EG, Koketsu Y. Big (pig) data and the internet of the swine things: a new paradigm in the industry. [cited 2025 Oct 9]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dx.doi.org/10.1093/af/vfz002\u003c/span\u003e\u003cspan address=\"10.1093/af/vfz002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 9 Oct 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStalder K, D\u0026rsquo;Allaire S, Drolet R, Abell C. Longevity in breeding animals. Diseases of swine. Wiley Chichester. 2012;10:50\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatterson J, Foxcroft G. Gilt management for fertility and longevity. Anim MDPI. 2019;9:434.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanz-Fern\u0026aacute;ndez S, Rodr\u0026iacute;guez-Hern\u0026aacute;ndez P, D\u0026iacute;az-Gaona C, Tusell L, Quintanilla R, Rodr\u0026iacute;guez-Est\u0026eacute;vez V. Evolution of Sow Productivity and Evaluation Parameters: Spanish Farms as a Benchmark. Vet Sci. 2024;11:626. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/vetsci11120626\u003c/span\u003e\u003cspan address=\"10.3390/vetsci11120626\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTani S, Pi\u0026ntilde;eiro C, Koketsu Y. Mortality and Survival Probability of Female Pigs in Southern European Commercial Breeding Herds. JAS. 2017;9:38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5539/jas.v9n4p38\u003c/span\u003e\u003cspan address=\"10.5539/jas.v9n4p38\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePosit team. RStudio: Integrated development environment for R [Internet]. Boston, MA: Posit Software, PBC; 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.posit.co/\u003c/span\u003e\u003cspan address=\"http://www.posit.co/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrenberth KE. What are the seasons? Bull - Am Meteorological Soc. 1983;64:1276\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDohoo IR, Martin W, Stryhn HE. Veterinary epidemiologic research [Internet]. 2009 [cited 2023 Nov 3]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://islandscholar.ca/islandora/object/ir:ir-batch6-2657\u003c/span\u003e\u003cspan address=\"https://islandscholar.ca/islandora/object/ir:ir-batch6-2657\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 3 Nov 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoving LL, Soede NM, Graat EAM, Feitsma H, Kemp B. Reproductive performance of second parity sows: Relations with subsequent reproduction. Livest Sci. 2011;140:124\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.livsci.2011.02.019\u003c/span\u003e\u003cspan address=\"10.1016/j.livsci.2011.02.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasaki Y, Koketsu Y. Variability and repeatability in gestation length related to litter performance in female pigs on commercial farms. Theriogenology. 2007;68:123\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.theriogenology.2007.04.021\u003c/span\u003e\u003cspan address=\"10.1016/j.theriogenology.2007.04.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBloemhof S, Mathur PK, Knol EF, van der Waaij EH. Effect of daily environmental temperature on farrowing rate and total born in dam line sows1. J Anim Sci. 2013;91:2667\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2527/jas.2012-5902\u003c/span\u003e\u003cspan address=\"10.2527/jas.2012-5902\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIida R, Pi\u0026ntilde;eiro C, Koketsu Y. Incidences and risk factors for prolapse removal in Spanish sow herds. Prev Vet Med. 2019;163:79\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.prevetmed.2018.12.016\u003c/span\u003e\u003cspan address=\"10.1016/j.prevetmed.2018.12.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIwagami M, Shinozaki T. Introduction to Matching in Case-Control and Cohort Studies. Ann Clin Epidemiol. 2022;4:33\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.37737/ace.22005\u003c/span\u003e\u003cspan address=\"10.37737/ace.22005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenchmarking Summaries |. Swine Management Software | PigCHAMP.com [Internet]. [cited 2025 Feb 27]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pigchamp.com/benchmarking/benchmarking-summaries\u003c/span\u003e\u003cspan address=\"https://www.pigchamp.com/benchmarking/benchmarking-summaries\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 27 Feb 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngland DC. Care of the Sow During Farrowing and Lactation. U.S. Department of Agriculture, Office of Governmental and Public Affairs; 1981.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIida R, Koketsu Y. Climatic factors associated with peripartum pig deaths during hot and humid or cold seasons. Prev Vet Med. 2014;115:166\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.prevetmed.2014.03.019\u003c/span\u003e\u003cspan address=\"10.1016/j.prevetmed.2014.03.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonckaert C, Bross\u0026eacute; C, Vandersmissen T, Caliskan N, Buys E, Chantziaras I, et al. Field Study on Sow Mortality in 15 Belgian Pig Farms. Veterinary Sciences. Multidisciplinary Digit Publishing Inst. 2025;12:603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/vetsci12070603\u003c/span\u003e\u003cspan address=\"10.3390/vetsci12070603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrolet R, D\u0026rsquo;Allaire S, Chagnon M. Some observations on cardiac failure in sows. Can Vet J. 1992;33:325\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraser AF. Studies on heat stress in pigs in a tropical environment. Trop Anim Health Prod. 1970;2:76\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02359573\u003c/span\u003e\u003cspan address=\"10.1007/BF02359573\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmerman JJ, editor. Diseases of swine. 10th ed. Chichester, West Sussex: Wiley-Blackwell; 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026rsquo;Allaire S, Drolet R. Longevity in breeding animals. Diseases of swine. Blackwell PublishingAmes. 2006;9:1011\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrolet R. [cited 2024 Jan 31]. Urinary System. Wiley, Ltd;; 2019. pp. 408\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/9781119350927.ch22\u003c/span\u003e\u003cspan address=\"10.1002/9781119350927.ch22\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Diseases of Swine [Internet].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKo Y-R, Lee S-R, Kim S-H, Chae H-D. Pelvic Organ Prolapse Is Associated with Osteoporosis in Korean Women: Analysis of the Health Insurance Review and Assessment Service National Patient Sample. Journal of Clinical Medicine. Volume 10. Multidisciplinary Digital Publishing Institute; 2021. p. 3751. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jcm10163751\u003c/span\u003e\u003cspan address=\"10.3390/jcm10163751\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi M-X, Yu Q. Primary osteoporosis in postmenopausal women. Chronic Diseases and Translational Medicine. Chin Med Association Publishing House. 2015;01:9\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cdtm.2015.02.006\u003c/span\u003e\u003cspan address=\"10.1016/j.cdtm.2015.02.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoss JW. Identification of putative factors contributing to pelvic organ prolapse in sows [Internet]. 2019 July. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://porkcheckoff.org/research/identification-putative-factors-contributing-pelvic-organ-prolapse-sows/\u003c/span\u003e\u003cspan address=\"https://porkcheckoff.org/research/identification-putative-factors-contributing-pelvic-organ-prolapse-sows/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoketsu Y. Retrospective analysis of trends and production factors associated with sow mortality on swine-breeding farms in USA. Prev Vet Med. 2000;46:249\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0167-5877(00)00153-7\u003c/span\u003e\u003cspan address=\"10.1016/S0167-5877(00)00153-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Liu X, Mo D, Chen Q, Chen Y. Analysis of reasons for sow culling and seasonal effects on reproductive disorders in Southern China. Anim Reprod Sci. 2015;159:191\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.anireprosci.2015.06.018\u003c/span\u003e\u003cspan address=\"10.1016/j.anireprosci.2015.06.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngblom L, Lundeheim N, Dalin A-M, Andersson K. Sow removal in Swedish commercial herds. Livest Sci. 2007;106:76\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.livsci.2006.07.002\u003c/span\u003e\u003cspan address=\"10.1016/j.livsci.2006.07.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTantasuparuk W, Lundeheim N, Dalin A-M, Kunavongkrit A, Einarsson S. Weaning-to-service interval in primiparous sows and its relationship with longevity and piglet production. Livest Prod Sci. 2001;69:155\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0301-6226(00)00256-6\u003c/span\u003e\u003cspan address=\"10.1016/S0301-6226(00)00256-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTummaruk P, Tantasuparuk W, Techakumphu M, Kunavongkrit A. Influence of repeat-service and weaning-to-first-service interval on farrowing proportion of gilts and sows. Prev Vet Med. 2010;96:194\u0026ndash;200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.prevetmed.2010.06.003\u003c/span\u003e\u003cspan address=\"10.1016/j.prevetmed.2010.06.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu F, Zhao W, Le HH, Cottrell JJ, Green MP, Leury BJ et al. Review: What have we learned about the effects of heat stress on the pig industry? animal. 2022;16:100349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.animal.2021.100349\u003c/span\u003e\u003cspan address=\"10.1016/j.animal.2021.100349\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristensen G, Vraa-Andersen L, Mousing J. Causes of mortality among sows in Danish pig herds. Vet Rec. 1995;137:395\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/vr.137.16.395\u003c/span\u003e\u003cspan address=\"10.1136/vr.137.16.395\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSt-Pierre NR, Cobanov B, Schnitkey G. Economic Losses from Heat Stress by US Livestock Industries1. J Dairy Sci. 2003;86:E52\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3168/jds.S0022-0302(03)74040-5\u003c/span\u003e\u003cspan address=\"10.3168/jds.S0022-0302(03)74040-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeumann E, Hall W, Stevenson M, Morris R, Ling Min Than J. Descriptive and temporal analysis of post-mortem lesions recorded in slaughtered pigs in New Zealand from 2000 to 2010. New Z Veterinary J Taylor Francis. 2014;62:110\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00480169.2013.853278\u003c/span\u003e\u003cspan address=\"10.1080/00480169.2013.853278\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y, Jia G, Xu X. Extreme high temperatures and heatwave events across Europe in 2023. Environ Res Commun IOP Publishing. 2025;7:021001. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/2515-7620/adae60\u003c/span\u003e\u003cspan address=\"10.1088/2515-7620/adae60\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRedazione L. Climate change Europe: record-breaking heat and drought in 2024 [Internet]. Rinnovabili. 2025 [cited 2025 Sept 1]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rinnovabili.net/environment/climate-change/climate-change-europe-record-breaking-heat-and-drought-in-2024/\u003c/span\u003e\u003cspan address=\"https://www.rinnovabili.net/environment/climate-change/climate-change-europe-record-breaking-heat-and-drought-in-2024/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 1 Sept 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRouges E, Ferranti L, Kantz H, Pappenberger F. European heatwaves: Link to large-scale circulation patterns and intraseasonal drivers. Int J Climatol. 2023;43:3189\u0026ndash;209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/joc.8024\u003c/span\u003e\u003cspan address=\"10.1002/joc.8024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpain climate. average weather, temperature, rain - Climates to Travel [Internet]. [cited 2025 Mar 19]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.climatestotravel.com/climate/spain\u003c/span\u003e\u003cspan address=\"https://www.climatestotravel.com/climate/spain\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 19 Mar 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgencia Estatal de Meteorolog\u0026iacute;a (Espa\u0026ntilde;a). \u0026Aacute;rea de Climatolog\u0026iacute;a y Aplicaciones Operativas. Resumen estacional climatol\u0026oacute;gico. Verano 2022. Agencia Estatal de Meteorolog\u0026iacute;a; 2022 [cited 2025 Sept 1]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://repositorio.aemet.es/handle/20.500.11765/13966\u003c/span\u003e\u003cspan address=\"https://repositorio.aemet.es/handle/20.500.11765/13966\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 1 Sept 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgencia Estatal de Meteorolog\u0026iacute;a (Espa\u0026ntilde;a). \u0026Aacute;rea de Climatolog\u0026iacute;a y Aplicaciones Operativas. Resumen estacional climatol\u0026oacute;gico. Verano 2023. Agencia Estatal de Meteorolog\u0026iacute;a; 2023 [cited 2025 Sept 1]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://repositorio.aemet.es/handle/20.500.11765/15181\u003c/span\u003e\u003cspan address=\"https://repositorio.aemet.es/handle/20.500.11765/15181\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 1 Sept 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgencia Estatal de Meteorolog\u0026iacute;a (Espa\u0026ntilde;a). \u0026Aacute;rea de Climatolog\u0026iacute;a y Aplicaciones Operativas. Resumen estacional climatol\u0026oacute;gico. Verano 2024. Agencia Estatal de Meteorolog\u0026iacute;a; 2024 [cited 2025 Sept 1]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://repositorio.aemet.es/handle/20.500.11765/16266\u003c/span\u003e\u003cspan address=\"https://repositorio.aemet.es/handle/20.500.11765/16266\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 1 Sept 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReporter TI. Sept. Spain\u0026rsquo;s Third-Hottest Year: A Snapshot of Climate Change in 2024 [Internet]. The International Reporter- Spanish news in English. 2025 [cited 2025 Sept 1]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://theinternationalreporter.com/spain-weather-hottest-year-dana-climate-change/\u003c/span\u003e\u003cspan address=\"https://theinternationalreporter.com/spain-weather-hottest-year-dana-climate-change/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 1 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnprecedented warmth. A look at Spain\u0026rsquo;s exceptional summer of 2022. Atmospheric Res Elsevier. 2023;293:106931. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosres.2023.106931\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosres.2023.106931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eExceptional heatwaves and mortality in Europe. Greater impacts since the coronavirus disease 2019 outbreak. Environ Pollution Elsevier. 2024;363:125058. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2024.125058\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2024.125058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCooke AS, Rivero MJ. Livestock heat stress risk in response to the extreme heat event (heatwave) of July 2022 in the UK [Internet]. bioRxiv; 2023 [cited 2025 Sept 1]. p. 2023.05.18.541284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2023.05.18.541284\u003c/span\u003e\u003cspan address=\"10.1101/2023.05.18.541284\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;n-Valls GE, Cortey M, Allepuz A, Illas F, Tello M, Mateu E. Introduction of a PRRSV-1 strain of increased virulence in a pig production structure in Spain: virus evolution and impact on production. Porc Health Manag. 2023;9:1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40813-022-00298-3\u003c/span\u003e\u003cspan address=\"10.1186/s40813-022-00298-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDee S, Deen J, Rossow K, Wiese C, Otake S, Joo HS, et al. Mechanical transmission of porcine reproductive and respiratory syndrome virus throughout a coordinated sequence of events during cold weather. Can J Vet Res. 2002;66:232\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDee S, Deen J, Rossow K, Weise C, Eliason R, Otake S, et al. Mechanical transmission of porcine reproductive and respiratory syndrome virus throughout a coordinated sequence of events during warm weather. Can J Vet Res. 2003;67:12\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLugo Mesa V, Quinonez Munoz A, Sobhy NM, Corzo CA, Goyal SM. Survival of Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) in the Environment. Veterinary Sciences. Multidisciplinary Digit Publishing Inst. 2024;11:22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/vetsci11010022\u003c/span\u003e\u003cspan address=\"10.3390/vetsci11010022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaiva RC, Moura CA, Thomas P, Haberl B, Greiner L, Rademacher CJ, et al. Risk factors associated with sow mortality in breeding herds under one production system in the Midwestern United States. Prev Vet Med. 2023;213:105883. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.prevetmed.2023.105883\u003c/span\u003e\u003cspan address=\"10.1016/j.prevetmed.2023.105883\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanderhaeghe C, Dewulf J, De Vliegher S, Papadopoulos GA, de Kruif A, Maes D. Longitudinal field study to assess sow level risk factors associated with stillborn piglets. Anim Reprod Sci. 2010;120:78\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.anireprosci.2010.02.010\u003c/span\u003e\u003cspan address=\"10.1016/j.anireprosci.2010.02.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSens Junior VL, Rosa ER, Fagundes DP, Peripolli V, Ulguim R da, Siqueira R et al. BP,. Risk factors associated with stillbirth in sows. Cienc Rural. Universidade Federal de Santa Maria; 2023;53:e20220080. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/0103-8478cr20220080\u003c/span\u003e\u003cspan address=\"10.1590/0103-8478cr20220080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRangstrup-Christensen L, Krogh MA, Pedersen LJ, S\u0026oslash;rensen JT. Sow-level risk factors for stillbirth of piglets in organic sow herds. animal. 2017;11:1078\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1751731116002408\u003c/span\u003e\u003cspan address=\"10.1017/S1751731116002408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNam NH, Sukon P. Risk Factors Associated with Stillbirth in Swine Farms in Vietnam. World\u0026rsquo;s Veterinary Journal. Volume 10. Турция, Эрзурум: Scienceline Publication Ltd.; 2020. pp. 74\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorges VF, Bernardi ML, Bortolozzo FP, Wentz I. Risk factors for stillbirth and foetal mummification in four Brazilian swine herds. Prev Vet Med. 2005;70:165\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.prevetmed.2005.03.003\u003c/span\u003e\u003cspan address=\"10.1016/j.prevetmed.2005.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanz M. What we see with sow mortality. 2002 [cited 2025 Mar 19]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.academia.edu/58150623/What_we_see_with_sow_mortality\u003c/span\u003e\u003cspan address=\"https://www.academia.edu/58150623/What_we_see_with_sow_mortality\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 19 Mar 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuro BB, Carnevale RF, Leal DF, Almond GW, Monteiro MS, Poor AP, et al. The importance of optimal body condition to maximise reproductive health and perinatal outcomes in pigs. Nutr Res Rev. 2023;36:351\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0954422422000129\u003c/span\u003e\u003cspan address=\"10.1017/S0954422422000129\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVargovic L, Athorn RZ, Hermesch S, Bunter KL. Improving sow welfare and outcomes in the farrowing house by identifying early indicators from pre-farrowing assessment. J Anim Sci. 2022;100:skac294. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jas/skac294\u003c/span\u003e\u003cspan address=\"10.1093/jas/skac294\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergman P, Gr\u0026ouml;hn YT, Rajala-Schultz P, Virtala A-M, Oliviero C, Peltoniemi O, et al. Sow removal in commercial herds: Patterns and animal level factors in Finland. Prev Vet Med. 2018;159:30\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.prevetmed.2018.08.010\u003c/span\u003e\u003cspan address=\"10.1016/j.prevetmed.2018.08.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoge MD, Bates RO. Developmental factors that influence sow longevity1. J Anim Sci. 2011;89:1238\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2527/jas.2010-3175\u003c/span\u003e\u003cspan address=\"10.2527/jas.2010-3175\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngblom L, Eliasson-Selling L, Lundeheim N, Bel\u0026aacute;k K, Andersson K, Dalin A-M. Post mortem findings in sows and gilts euthanised or found dead in a large Swedish herd. Acta Vet Scand. 2008;50:25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1751-0147-50-25\u003c/span\u003e\u003cspan address=\"10.1186/1751-0147-50-25\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuran CO. Sow Mortality. Food Anim. 2001;23:S76\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStalder KJ, Lacy RC, Cross TL, Conatser GE, Darroch CS. Net Present Value Analysis of Sow Longevity and the Economic Sensitivity of Net Present Value to Changes in Production, Market Price, Feed Cost, and Replacement Gilt Costs in a Farrow-to-Finish Operation. Prof Anim Sci. 2000;16:33\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15232/S1080-7446(15)31658-2\u003c/span\u003e\u003cspan address=\"10.15232/S1080-7446(15)31658-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStalder K, Lacy C, Cross T, Conatser G. Financial impact of average parity of culled females in a breed-to-wean swine operation using replacement gilt net present value analysis. JSHAP. 2003;11:69\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.54846/jshap/357\u003c/span\u003e\u003cspan address=\"10.54846/jshap/357\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStalder K, Knauer M, Baas T, Rothschild M, Mabry J. Sow Longevity. Pig News Inform. 2004;25:53\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatterson JL, Beltranena E, Foxcroft GR. The effect of gilt age at first estrus and breeding on third estrus on sow body weight changes and long-term reproductive performance. J Anim Sci. 2010;88:2500\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2527/jas.2008-1756\u003c/span\u003e\u003cspan address=\"10.2527/jas.2008-1756\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Parity, reproductive performance, farrowing risk, stillbirths, mummified fetuses, gilt management, herd longevity, sow death causes","lastPublishedDoi":"10.21203/rs.3.rs-8865197/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8865197/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\nSow mortality has been increasing in recent years, posing significant challenges to commercial pig production. High mortality affects animal welfare, farm productivity, and causes substantial economic losses. While previous studies have identified risk factors such as high parity, seasonal effects, and farrowing-related complications, limited research has quantified mortality risk at different reproductive stages in Spain. This study aims to analyze sow mortality patterns and identify associated risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nAnalysis of production records from 16 Spanish farms revealed that 55.59% of sow deaths occurred before farrowing, while 44.41% occurred after farrowing. Mortality risk peaked during late gestation (105–118 days) and again during the first and after the fourth weeks post-farrowing.\u003c/p\u003e\n\u003cp\u003eSeveral risk factors were identified at farm and individual levels. Internal gilt replacement was associated with increased mortality risk. Before farrowing, sows with parity ≥3 and single-service sows exhibited higher mortality. After farrowing, increased mortality was observed in parity 1 sows and those with repeat breeding. Seasonal effects showed highest mortality rates in sows bred in spring and farrowing in summer.\u003c/p\u003e\n\u003cp\u003eMatched case-control analysis revealed that parity 0 sows had a younger age at first service, and deceased sows had shorter gestational length, fewer piglets born, born alive, and a higher incidence of stillbirth fetuses at their last litter before their death. Comparing their lifetime performance, dead sows had younger gilt age at first service, fewer parity at removal, higher average number of piglets born, born alive, born still and born mummified per parity, but they had fewer weaned piglets and nonproductive days.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003cbr\u003e\nThis study identifies late gestation and the first week postpartum as critical windows for sow survival, warranting enhanced peripartum care. Internal gilt replacement and parity extremes emerged as key mortality risk factors. Seasonal vulnerability around summer farrowing underscores the need for environmental mitigation strategies. Repetitive services may signal underlying health issues and increasing postpartum mortality risk. These patterns emphasize integrating early reproductive indicators into sow management and culling decisions.\u003c/p\u003e","manuscriptTitle":"Sow mortality risk factors at different reproductive stages: an analysis of production data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 21:25:02","doi":"10.21203/rs.3.rs-8865197/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6624149f-9718-427e-83e5-42abcd30d487","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T11:24:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 21:25:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8865197","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8865197","identity":"rs-8865197","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}