Integrating heterogeneous data to address endemic diseases in broiler production: insights from a Polish case study

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However, endemic contagious diseases are multifaceted and complex. They are rarely monitored on a large scale. This complexity hinders their mitigation, as timely information about their distribution and knowledge about their impact on production performance is scarce. This study aimed to evaluate whether data routinely produced by the Polish broiler industry, the first European meat producer, could be reused to generate knowledge about those diseases and provide stakeholders with contextual information to improve their disease management. Results The study reused a dataset collected by a large producer and a veterinary laboratory, which implemented a screening program at the end of the production cycle. The high-dimensional dataset covered 115 flocks produced between 2018 and 2023 across Poland. It contained information on production indicators, health indicators, necropsy lesions, and a list of evidence of infection or infestation by a diverse range of aetiological agents (bacterial, viral, and Eimeria ). The screened flocks, despite strong production performance indicators, presented a higher mortality rate and a large diversity of pathogens. The cluster analysis enabled the identification of three flock profiles, connecting the observation variables (health, production indicator, necropsy lesions) to the aetiological agents. Flocks from the first cluster were described as a flock with high rates of fibrinous lesions, with a high condemnation rate associated with the identification of E. coli. The second cluster was defined by high production performances but also higher rates of femoral head necrosis. The flocks from the last cluster had lower production performance, showing evidence of strong infestation by Eimeria spp. and evidence of avian metapneumovirus circulation. Conclusion The study is an example of how high-dimensional data produced by the broiler industry can be reused and integrated to create contextual knowledge for farmers and veterinarians about endemic contagious diseases. Access to this timely contextual knowledge is essential to enhance disease prevention and management efforts for farmers, veterinarians, and broiler industry stakeholders. Endemic disease Poultry Data reuse Cluster Analysis Health Production performance Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Since the 1970s, the global poultry industry has expanded continuously to meet a growing demand for poultry meat [ 1 ]. This production growth could not have been achieved without a steep increase in production efficiency due to growing technical capacities in genetics, housing, nutrition, biosecurity and health management [ 2 ]. Despite these achievements, the broiler production sector still faces multiple challenges associated with increased resource competitiveness, growing threats of emerging infectious diseases, and higher consumer expectations concerning food safety, animal welfare, and environmental impacts [ 2 – 4 ]. Addressing these challenges requires the industry to evolve while maintaining low production costs to meet market demand. One of the main areas of improvement for increasing production efficiency lies in mitigating the impact of endemic contagious diseases [ 5 ]. However, these diseases are multifaceted and complex, involving interactions between pathogens, animal genetics, and the environment, varying their impacts across the diversity of contexts [ 2 , 6 ]. This complexity hinders the accessibility of up-to-date information regarding the status and production impact of poultry endemic contagious diseases [ 6 ], despite their necessity to address the sector’s persistent challenges. Implementing epidemiological studies on endemic contagious diseases and their impacts can be expensive, time-consuming, and often too slow to keep pace with context-specific trends [ 7 ]. Using pre-existing data sources to generate more timely and targeted information for the industry has been described as a cost-effective solution compared to some traditional approaches [ 3 , 7 ]. In the broiler production sector, it has been observed that data collection and digitalisation have also escalated with the expansion of the technical capacities. Indeed, stakeholders across the value chain increasingly use data to support their activities with different intensity levels [ 8 ]. These data offer timely insight into farm and poultry production, as well as the activities of other value chain stakeholders, which could be key to optimising endemic contagious disease management [ 9 , 10 ]. Specifically, integrating production and health data could be particularly valuable to inform on the main endemic contagious disease burden, providing data-driven information to prioritise investments and action [ 6 , 11 ]. Data health solutions are being developed in precision livestock farming to meet this opportunity [ 8 , 10 ]. However, they mainly concentrate on gathering new data rather than integrating existing data sources [ 7 , 9 ]. These approaches thus do not address the specific issue encountered by poultry producers, where data about a flock can be dispersed across multiple actors throughout the production chain (e.g., flock manager, slaughterhouse, feed mill, veterinarian, diagnostic laboratory, and competent governmental authorities), even in the context of highly integrated production systems. If integrating these data holds multiple promises to support the industry in optimising broiler health, it is also associated with challenges regarding data ownership, accessibility, and integration [ 3 ]. Furthermore, more generally, farmers’ adoption of digital technologies has been hindered by high cost, lack of comprehensive technological offer and trust that these technologies will not cause them harm, requiring strong collaboration to bridge the gap [ 10 , 12 ]. Before significantly investing in data integration, researchers need to demonstrate to producers the value of data integration in terms of support for production and health management [ 3 , 13 , 14 ]. Poland is Europe's largest poultry meat producer, with 2,746 thousand tonnes of meat produced in 2023 [ 15 , 16 ]. Polish producers suffer the same constraints as the rest of the industry, but operate in an environment characterised by rising production costs and overall volatility of raw material prices [ 17 ]. Moreover, the Polish broiler production is not highly integrated despite its size [ 17 ]. This fragmented structure fosters a highly competitive environment, breeding distrust and hindering the collection and integration of data across the value chain. In this context, comprehensive information about poultry endemic contagious diseases in Poland, such as coccidiosis, infectious bronchitis virus (IBV), infectious bursal disease virus (IBDV), or avian metapneumovirus (aMPV), is scarce. The present study aims to improve our understanding of the major endemic contagious diseases affecting the Polish production system by reusing high-dimensional data from broiler producers and a diagnostic laboratory. To achieve this, the study identifies the predominant health and production patterns amongst the studied broiler flocks using clustering techniques. It discusses their potential association with specific aetiological agents to define strategies to enhance animal production, health, and welfare. Furthermore, it assesses the quality of the information produced. Finally, it discusses its application to Polish broiler stakeholders in a context where data integration and reuse for health management purposes are relatively uncommon. Methodology Data sources All the data came from pseudo-anonymised poultry farms growing Ross308 broilers in the northeast region of Poland between 2018 and 2023. They were made of two distinct datasets: the “chicken farm” dataset provided by poultry producers, and the “laboratory” datasets provided by a veterinary diagnostic laboratory (see Fig. 1 ). The “chicken farm” dataset included standard indicators used to evaluate performance of broiler flocks (i.e., mortality, death on arrival at the slaughterhouse, mean age at slaughter, foot-pad lesion scores, etc.) and information about vaccinations and antibiotic treatments administered during the flock production cycle (i.e., name of the product or active substance, day of treatment, and route of administration). This dataset collected information from 59 farms, but the amount of information available for each farm varied between one and 31 production cycles. Moreover, the treatment data was available for less than 7% of the flocks with production performance data. The “laboratory” dataset was related to tests performed during the last week of production cycles as part of a screening program proposed to farmers by the veterinary diagnostic laboratory. This dataset collected information from 183 flocks coming from 98 farms. Necropsy observations and bacteriological, parasitological, and virological test results were available for five chickens randomly sampled per flock. On the same day, blood/serum from 23 birds of each screened flock was also randomly collected to be analysed serologically for three diseases (the same as the one investigated in the virological test). Technical details on the laboratory test used are available in Additional file 1. Data processing The data was then processed into 78 variables grouped into eight themes. Half of the themes were related to the data provided by the broiler producers (i.e., ‘Context’, ‘Economic indicator’, ‘Production performance’, ‘Health and Welfare’), three to the data provided by the laboratory (i.e., ‘Necropsy lesions’, ‘Bacteria status’, ‘Eimeria status’) and one (i.e. ‘Viral status’) was based on a combination of information extracted from the two datasets. The themes and their associated variables are described below and summarised in Table 1 . Table 1 Description of the integrated data after cleaning and processing into eight themes and 73 variables. Themes Variable Type* Definition Contextual Season Spring Production mainly occurred from the 1st of March until the 31st of May Summer Production mainly occurred from the 1st of June until the 31st of August Autumn Production mainly occurred from the 1st of September until the 30th of November Winter Production mainly occurred from the 1st of December until the 28th of February Region PL9 Farm’s postal code was included in region PL9 -Masovian voivodeship PL6 Farm’s postal code was included in region PL6- Północny region PL8a Farm’s postal code was included in southern region PL8 - Lublin Voivodeship PL8b Farm’s postal code was included in northern region PL8 - Podlaskie Voivodeship Farm size Small Farm with 1 or 2 chicken houses Large Farm with over two chicken houses ATB use Y Antibiotics were used during the production cycle N Antibiotics were used during the production cycle Economic EPEF Continuous European production efficiency factor Production performance Mean age at slaughter Continuous Mean age at slaughter of birds in days Mean weight at slaughter Continuous Mean measured weight of birds slaughtered in Kg FCR (Feed conversion rate) Continuous Total weight of feed distributed to the flock divided by the total weight of birds sold Health and welfare Mortality Interval Number of chicks ordered at the hatchery minus the number of birds sold to the slaughterhouse divided by the number of birds sold (%) DOA (Dead on arrival) Interval Number of birds dead on arrival (transport) divided by the number of birds sold (%) Condemnation Interval Number of birds condemned divided by the number of birds sold (%) FPLS (Foot-pad lesion score) Interval Sum of the weighted product of birds with foot-pad lesions of grade 1 (*0.5) and grade 2 (* 2) amongst a hundred randomly sampled birds Necropsy lesion 40 variables listed in Table 4 N The validated lesion wasn’t observed in the screened flocks Y The validated lesion was observed in at least one screened flock Bacteria status 19 variables listed in Table 5 N The bacteria group wasn’t identified in the screened flocks Y The bacteria group was identified in at least one of the screened flocks Eimeria status Variable defined for 4 intestinal sections No No evidence of circulation - the sum of all grades of screened birds equals to 0 Low Low evidence of circulation - the sum of all grades of screened birds is between 1 and 3 High High evidence of circulation- the sum of all grades of screened birds is over 3 Virus circulation Variable defined for 3 endemic viral diseases. No No virus field strain circulation; see Additional file 2 SUS Suspicion of any virus field strain circulation, see Additional file 2 HSUS High suspicion of IBV virus field strain circulation; see Additional file 2 Yes Confirmed field strain circulation; see Additional file 2 Here add : Table 1 : Description of the integrated data after cleaning and processing into eight themes and 73 variables. Theme: Context Four contextual variables were defined: ‘season’ and ‘region’ of production, ‘farm size’ and Antibiotic use (‘ATB use’). The season of production was determined based on the dates of flock production. A four-season model was used, with spring beginning on March 1st, summer on June 1st, autumn on September 1st, and winter on December 1st. Flocks with a production period overlapping two different seasons were assigned to the season that overlapped the most with the production period (most days). The production region was based on postal codes and the definition of the major socio-economic regions as defined in the European nomenclature of territorial units for statistics (NUTS 1), except for one, the NUTS coded ‘PL8’, which was divided into two parts: north and south. The farm size was defined based on the number of chicken houses [1–2, or over 2]. Finally, flocks were categorised based on the use or non-use of antibiotics during the production cycle. Theme: Production performance Three flock performance variables were identified and based on existing indicators which had been defined and used by the poultry farmer to monitor their flocks: ‘mean age at slaughter’ in days, ‘mean weight at slaughter’ in Kg and the ‘Feed Conversion Rate’ (‘FCR’). The ‘mean age at slaughter’ and ‘mean weight at slaughter’ were defined in the dataset and, in the usual broiler value chain, are calculated by the slaughterhouse as the geometric mean of all broilers' age and weight sent to the slaughterhouse during the flock’s thinning and final clearance. The ‘FCR’ is a standard indicator calculated based on available information in the dataset and is defined as the total weight of feed (Kg) provided to the flocks divided by the total weight of birds sold at the slaughterhouse. Theme: Health and welfare Four health and welfare variables were defined. ‘Mortality’ was expressed as a percentage and defined as the difference between the number of chicks bought at the hatchery and the number of birds sold at slaughter (before death in transport and condemnation) divided by the number of birds sold at slaughter. The ‘DOA’ (Dead on arrival) percentage was defined as the number of birds dead on arrival at slaughter (during transport) divided by the total of birds sold at slaughter. The percentage of ‘condemnation’ was defined as the number of birds condemned at the slaughterhouse divided by the number of birds sold to the slaughterhouse. The ‘FPLS’ (Foot-pad lesion score) variable of each flock is a weighted indicator based on the individual foot-pad lesion scores of a hundred birds sampled at the slaughterhouse, as required by the EU regulation 1 . Each bird is given a foot-pad lesion score: ‘0’ if no lesions were found, ‘1’ if the lesions were not too extensive and ‘2’ if they were. Then, the foot-pad lesion score of the flock is calculated by summing two products: 0.5 times the number of birds with a foot-pad lesion of grade ‘1’ and two times the number of birds with a foot-pad lesion of grade ‘2’. Theme: Economic indicator Broiler producers use the European Production Efficiency Factor (EPEF) indicator to compare flocks. This indicator is calculated based on the others presented above: ‘mortality’, ‘FCR’, ‘mean age at slaughter’ and ‘mean weight at slaughter’. It’s the ‘mean weight at slaughter’ times the survival rate (a hundred minus the ‘mortality’) divided by the product of the ‘FCR’, ‘mean day of slaughter’ and a constant of 0.1 to adjust for units. Theme: Necropsy lesions Thirty-five variables linked to necropsy investigations were identified. A list of 115 different necropsy lesions was available in the raw data. These lesions were grouped into nine anatomic systems or organs (e.g., cardiovascular system, spleen) and thirteen lesion types (e.g., fibrinous lesions, changes in the organ structure). Each group of lesions was turned into a binary variable coded as follows: ‘Y’ if at least one lesion from that group was observed in at least one of the five screened flocks, and ‘N’ if no lesion was observed in the screened flocks. The 40 groups are described in detail in Additional file 2. Furthermore, for each observed lesion, a confidence score was defined by pathologists based on their confidence in the fact that the lesion was present in the animal before collection or, rather, resulted in post-mortem deterioration of the animal. The scale was defined as follow: ‘high’ means pathologists were confident that the lesion existed antemortem, ‘low’ means pathologists had a low confidence that this lesion existed antemortem, ‘very Low’ means pathologists believed that the lesions did not exist antemortem and were rather due to post-mortem deterioration of the animal. Only the 18 lesions associated with a confidence score of ‘high’ and ‘low’ were retained for the analysis. Theme: Bacteria status Nineteen variables related to the bacterial status of the flocks were built using 41 different bacteria taxa available in the raw data. These bacteria taxa could correspond to varying levels of classification of bacteria from the most detailed (ie, a bacteria species subtype) to the least (ie, bacteria genus). If a high classification level (such as genus) was used, all sub-levels were classified using the lowest common level (in this case, the genus). The following information was available for each screened flock: the observed bacteria taxa, the number of screened birds with samples from necropsy lesions where the bacteria grew, the observed bacteria growth intensity, and the sample's localisation in which the bacteria were identified (see Additional file 2 for more information). In the case of ‘ Escherichia coli’ (E. coli) and ‘ Clostridium perfringens’ , due to their ubiquity and the high probability of cross-contamination, only samples from internal organs (heart, liver, spleen and bone marrow) were kept for the analysis. Then, only when the growth intensity was over one in these samples was the result interpreted as the true presence of either bacteria. Finally, each variable was defined as a binary variable where ’Y’ means at least one of the bacteria from that taxa was identified in at least one of the birds of the screened flock, and ‘N’ means no bacteria taxa were found in the screened flock. Theme: Eimeria status Four variables related to the Eimeria status were defined based on the Eimeria species' preferred tissue tropism in the intestines. Indeed, Eimeria species present tissue tropism, meaning that parasite localisation is usually associated with a specific Eimeria species: E. acervulina with the duodenal loop, E. maxima with the Meckel’s diverticulum, E. tenella with the cecum and E. brunetti with the rectum [ 19 , 20 ]. In the raw data, each section of the intestines of each screened bird was given an infestation grade. This grade was defined at ‘0’ when no oocysts were found in the specific intestinal section, ‘1’ when 1 to 9 oocysts were observed, ‘2’ when 10 to 50 oocysts were observed, ‘3’ when more than 50 oocysts were observed. Each Emeria variable was turned into a categorical variable made of three levels defined according to the sum of the grade of Eimeria infestation across all five necropsied chickens: ‘no infestation’ if the sum equals 0, ‘low infestation’ if the sum is between one and three, ‘high infestation’ if the sum is above three. Theme: Viral circulation One variable was created for each of the three endemic viruses considered important for poultry producers: infectious bronchitis virus (IBV), infectious bursal disease virus (IBDV), and avian metapneumovirus (aMPV). Virus circulation was estimated based on information available on vaccine type application, geometric mean titres of the serological samples, and RT-PCR results. Four categories for virus circulation were defined: ‘no’ if no virus circulation, ‘SUS’ if virus circulation is suspected, ‘HSUS’ if the virus circulation is highly suspected (category only defined to quantify IBV field circulation), and ‘yes’ if the virus circulation is confirmed. The definitions of these categories for each virus are available in Additional file 2. Data analyses Data description All data processing and calculations were performed using R (v.4.1.1) software [ 18 ]. The production performances and health characteristics of the screened flocks were described, and outliers, uncorrelated variables and rare health events were identified. The correlation between quantitative variables was evaluated using Spearman correlation. The representativity of the screened flocks compared to other flocks was assessed by comparing the mean production performances of the screened flocks with the ones from the recorded flocks using a two-tailed Welch's two-sample t-test. Multifactor analysis and Hierarchical Clustering Multifactor analysis (MFA) is a form of exploratory dimensional multivariate analysis that explores the relationship between quantitative and/or qualitative mixed variables by reducing the data to a few dimension that retains most of the data variability. It was first performed to identify the links between the variables and groups of variables and was then followed by a hierarchical clustering (HC) analysis with k-means consolidation. The HC is a means to visualise the variability and similarities between individuals (i.e., flocks) regularly used in veterinary epidemiology [ 21 – 26 , 7 , 27 ]. Using MFA before HC allows the compilation of the Euclidean distance between groups, individuals, and variables and is used to reduce the number of variables to be included in the HC while mitigating the impact of multicollinearity [ 28 ]. The analyses were achieved through the FactoMineR package (v.2.11) [ 28 ]. For the MFA, due to the variability of units, all the continuous variables were centred and normalised. For the ‘FPLS’, the missing quantitative data were replaced by their mean. Binary variables were excluded before conducting the MFA when one of the categories encompassed less than 3% of the records, as they would not inform clustering. All remaining variables and individuals were classified as either active or supplementary. Active variables and individuals are used to construct the high-dimensional Euclidean space, while supplementary variables are only projected afterwards onto it. They are, therefore, independent of the created Euclidean space. Supplementary individuals and variables were identified according to different criteria. First, outliers, defined as individuals (flocks) with very distinctive characteristics that contributed disproportionately to the first dimensions of the MFA compared to others, were set as supplementary individuals. Second, quantitative variables with no collinearity with other quantitative variables or calculated from other quantitative variables were kept as supplementary variables. Third, themes that could be used to interpret the dimensional space were defined as supplementary. All other variables were defined as active. Finally, the results of the MFA were described and more specifically, the variables’ contribution to the first two dimensions and their link to the supplementary qualitative variable based on an analysis of variance (F-test per variable and two-tailed Student’s t-test for the categories). The HC was then performed on the MFA projection. Clusters were identified from a dendrogram based on the Ward’s criterion [ 28 ]. The optimal number of clusters was inferred from the minimum ratio between the within-group inertias of two successive clusters. The clusters' clarity, coherence, and interpretability were the criteria used for their validation. Flocks creating a cluster of a single individual were set as a supplementary variable, and the analysis was rerun. To better describe the variability and similarities between the individuals, a two-tailed hypergeometric test for qualitative variables was used to identify the overrepresentation of variables in each cluster, and a two-tailed t-test was used to compare whether the mean of the quantitative variable of the cluster is equal to the overall mean [ 28 ]. Results Descriptive analysis Screened flocks' production and health, and welfare indicators and contextual qualitative descriptors The final dataset used for the analysis (i.e., the 115 ‘screened flocks’) contained data collected between January 2022 and April 2023. The median of the number of chicken houses per farm was two (minimum = 1, maximum = 21). The median number of flocks screened per farm was two (minimum = 1, maximum = 5). Table 2 provides contextual information (i.e., farm type, region, season, ATB usage) about the screened flocks. Table 2 Distribution of the contextual variables amongst the screened flocks (n = 115). Contextual variables Number of flocks % of flocks Farm type … 1–2 chicken house 50 44% … >2 chicken houses 65 56% NUTS1 … PL6 6 5% … PL8 South 25 22% … PL8 North 14 12% … PL9 70 61% Season … Autumn 21 18% … Spring 37 32% … Summer 32 28% … Winter 25 22% ATB use … Y 87 76% … N 28 24% An overview of the production, health, and welfare indicators of the screened flocks is provided in Table 3 . Information on foot-pad lesions was missing in 6% of the screened flocks (n = 7). The analysis allowed the identification of one flock with extreme values: very high ‘mortality’ of 40.69%, ‘FCR’ of 3.29, an ‘EPEF’ of 84.59 and a mean ‘age at slaughter’ of 42 days. No additional information from the dataset could explain the mortality rate observed in this specific flock, implying that it is due to an event unrelated to the health issues explored in the available data. This flock was therefore considered an outlier for the rest of the analysis. Table 3 Description of the quantitative variables for the screened flocks and recorded flocks. Performance or health and welfare indicators Mean SD Median Min Max Screened flocks (n = 115) EPEF 388.59 54.69 395.64 84.59 487.84 Mortality (%)** 6.010 5.430 5.080 0.620 40.690 DOA (%) 0.004 0.003 0.003 0.001 0.018 Condemnation (%) 0.590 0.500 0.470 0.130 3.440 Mean weight at slaughter (Kg) 2.520 0.180 2.540 1.700 2.990 Mean age at slaughter (Day) 38.910 1.410 38.880 35.000 42.030 FCR 1.590 0.180 1.570 1.290 3.190 FPLS* (n = 108) 64.720 37.090 61.000 0.000 152.000 Recorded flocks (n = 1,697) EPEF 403.68 51.56 405.52 84.59 771.57 Mortality (%)** 4.450 3.420 3.930 -18.150 40.690 DOA (%) 0.003 0.003 0.003 0.000 0.088 Condemnation (%) 0.570 0.350 0.480 0.050 3.440 Mean weight at slaughter (Kg) 2.570 0.160 2.580 1.700 3.000 Mean age at slaughter (Day) 39.100 1.610 39.000 30.000 46.000 FCR 1.570 0.130 1.570 0.810 3.190 FPLS* (n = 1,412) 61.900 40.630 56.000 0.000 200.000 For the tables’ column name: SD as standard deviation, max as the maximum, min as the minimum. For the variable names: EPEF is the european production efficiency factor, DOA is the death on arrival, FCR is the feed conversion ratio, and FPLS is the foot-pad lesion score. *FPLS are the only variables with missing information; the number of the flocks with available data is given in parentheses. **Mortality is a calculated value based on the number of chickens ordered at the hatchery and the final number of chickens sold at the slaughterhouse, meaning that the mortality could be negative due to specific unrecorded events. For example, this could occur during the delivery of additional chicks from the hatchery or the introduction of additional chickens by the farmer during the production cycle. The relationships between quantitative variables were further explored by examining correlations among them (Fig. 2 .A), meaning variables amongst the following themes: ‘Economic indicator’, ‘Production performance’ and ‘Health and welfare’. ‘EPEF’ was the single quantitative variable strongly correlated (higher than 0.5) with the others. Indeed, it is calculated from most other quantitative variables and was kept as a supplementary variable in the analysis. Amongst the remaining quantitative variables, the highest correlation was found between ‘DOA’ and ‘condemnation’ (|r| = 0.424; p < 0.001). All the other significant correlations (p < 0.05) identified between variables had a small correlation coefficient (|r| 0.2). These variables were defined as active variables for the rest of the analysis. On the other hand, ‘FPLS’ (‘Foot-pad lesion scores’) was the only variable without any significant correlation with any of the other variables and, as such, was defined as a supplementary variable for the next step of the analysis. Figure A contains the screened flocks (n = 115); in Figure B, recorded flocks were used for the analysis (n = 1697). The screened flocks showed significantly lower performance and health compared to the recorded flocks for the same ‘mean age at slaughter’: the ‘weight at the slaughter’ of the screened flocks was lower (p < 0.001), while ‘mortality’ and ‘DOA’ were higher (p < 0.001 and p < 0.05, respectively). Furthermore, the dataset’s correlation matrices changed depending on the group of flocks considered, suggesting that the screened flocks may not be representative of the entire flock population, the recorded flocks (see Fig. 2 ). Indeed, the correlation among the quantitative variables was generally weaker in the recorded flocks compared to the screened flocks, with six comparisons losing significance (|r|< 0.2). However, a new correlation between age and weight at slaughter became significant (|r| = 0.245). Laboratory qualitative data An overview of the main screening results (‘Necropsy lesions’, ‘Bacteria status’, ‘Eimeria status’, and ‘Viral circulation’) is provided in Table 4 , Table 5 , Table 6 and Table 7 . ‘Necrosis or ulcer of the musculoskeletal system’ lesions, regrouping the common locomotor lesions ‘femoral head necrosis’ and ‘feet-pad dermatitis’, were observed in almost all the screened flocks (n = 106, 92.2%). Moreover, the majority of the screened flocks had at least one chicken presenting one of the high confidence score necropsy lesions: ‘uroliths in the ureters’ (n = 92, 80.0%), and ‘change in the composition (enlarged) of kidneys and/or ureters’ (n = 87, 75.7%). ‘Respiratory vascular congestion’ (hyperaemia or ecchymosis) was the only lesion associated with a low confidence score observed in most flocks (n = 89, 77.4%). Seventeen lesions were observed in less than 3% of the flocks. The corresponding variables were excluded from the analysis due to their scarcity. Table 4 Frequency of necropsy lesions observed in the 115 screened flocks. System Lesion type Confidence score Number of flocks % of flocks Musculo-skeletal Necrosis - ulcer High 106 92.2 Kidney and ureters Urates/ Uroliths High 92 80.0 Kidney and ureters Change in composition High 87 75.7 Spleen Change in composition High 48 41.7 Liver Fibrin High 30 26.1 Respiratory Fibrin High 30 26.1 Cardio-vascular Fibrin High 29 25.2 Celomic cavity Fibrin High 15 13.0 Liver Change in composition High 11 9.6 Musculo-skeletal Vascular congestion High 11 9.6 Gastrointestinal Empty or abnormal content High 10 8.7 Musculo-skeletal Swelling or oedema High 4 3.5 Respiratory Vascular congestion Low 89 77.4 Spleen Vascular congestion Low 54 47.0 Liver Vascular congestion Low 48 41.7 Kidney and ureters Vascular congestion Low 40 34.8 Cardio-vascular Thinning, distended Low 35 30.4 Respiratory Swelling or oedema Low 20 17.4 Cardio-vascular Change in composition Low 11 9.6 Cardio-vascular Swelling or oedema Low 8 7.0 Only lesions with a high or low confidence score are represented. Lesions associated with three or fewer flocks are also not presented, but available in the Additional file 3. Amongst the thirteen bacteria taxa identified in at least 4 screened flocks (Table 5 ), ‘ E. coli’ was isolated in internal organs of one of the randomly selected flocks in almost two-thirds of the flocks (60.9%). The other bacteria taxa found in more than a fourth of the flocks were ‘ Enterococcus faecalis’ (29.6%), ‘Staphylococcus spp.’ (27.8%) and ‘ Clostridium perfringens’ (26.1%). In contrast, six rare bacteria taxa were identified in less than 3% of the flocks and were excluded from the dataset for the rest of the analysis. Table 5 Frequency of bacteria taxa (species or genera) amongst the 115 screened flocks Bacteria groups (species or genera) identified Number of flocks % of flocks Escherichia coli* 70 60.9 Enterococcus faecalis 34 29.6 Staphylococcus spp 32 27.8 Clostridium perfringens* 30 26.1 Enterococcus cecorum 25 21.7 Ornithobacterium rhinotracheale 23 20.0 Enterococcus faecium 18 15.7 Bordetella avium 14 12.2 Gallibacterium anatis 11 9.6 Riemerella anatipestifer 8 7.0 Bordetella hinzii 7 6.1 Klebsiella pneumoniae 5 4.3 Enterococcus hirae 4 3.5 Bacteria taxa associated with three or fewer flocks are not presented, but available in the Additional file 3. The asterisk highlights that the presence of E.coli and Clostridium perfringens was defined a bit differently than for the other bacteria due to its ubiquity, see the methodology for further details. A majority of the screened flocks (n = 75, 65.2%) had no sign of Eimeria infestation. The cecum preferred by ‘ E. tenella’ was the portion of the intestines most often colonised by oocysts (Table 6 ): 30.4% (n = 35) of the flocks presented evidence of colonisation in at least one chicken. In contrast, the duodenum preferred by ‘ E. acervulina’ was the section of the intestines that was the least colonised (n = 11, 9,6%). Table 6 Frequency of Eimeria infestation grade amongst the 115 screened flocks. Parasite infestation Number of flocks % of flocks Duodenum … High 1 1% … Low 10 9% … No 104 90% Meckel Diverticulum … High 3 3% … Low 15 13% … No 97 84% Cecum … High 12 10% … Low 23 20% … No 80 70% Rectum … High 10 9% … Low 17 15% … No 88 77% The estimated circulation of IBDV, IBV and aMPV viruses is available in Table 7 . Almost half (42%, n = 48) of the sampled flocks showed evidence of field IBV virus circulation. Among those flocks, 70.8% were positive to the VAR 2 genotype (n = 34), 22.9% to the 793B genotype (n = 11),10.4% to the D274 genotype (n = 5), 4.2% to the Mass genotype (n = 2), 2.1% to the QX genotype (n = 1) and IB80 genotype (n = 1). All results are available in Additional file 3. The aMPV circulated in 20% of the flocks (n = 23) and IBDV in 2% (n = 2). However, virus genotypes for IBDV and aMPV were not explored further in the categories developed in Additional file 2. Table 7 Frequency of virus circulation evidence amongst the 115 screened flocks. Virus circulation Number of flocks % of flocks Avian metapneumovirus (aMPV) … No 44 38% … SUS 48 42% … Yes 23 20% Infectious bronchitis virus (IBV) … No 40 35% … SUS 13 11% … HSUS 14 12% … Yes 48 42% Infectious bursal disease virus (IBDV) … No 75 65% … SUS 38 33% … Yes 2 2% MFA (Multifactor analysis) The MFA was performed on all 115 screened flocks with the 53 variables available. All the variables associated with the aetiological and contextual themes were set as supplementary variables (i.e. contextual, economic indicators, bacteria status, Eimeria status, and virus circulation). Indeed, the information they contain can be used to characterise the observations related to production performance, health and welfare and necropsy lesions. Furthermore, two quantitative variables (i.e., ‘FPLS’ and ‘EPEF’) and one flock (the identified outlier) were defined, respectively, as supplementary variables and as supplementary individuals. A first attempt at HC on this data set created a cluster containing a single flock. This flock had the highest condemnation rate (3.4%), the lowest mean weight at slaughter (1.70 kg) and the lowest mean age at slaughter (35 days), meaning that the production cycle was stopped early due to a specific event. As such, it was defined as an outlier and set as a supplementary individual. The final analysis was therefore done on 27 active variables and 113 active individuals. The first 21 dimensions obtained with the MFA represented 95% of the dataset's cumulative variability. The projection on the first two dimensions of the quantitative variables, the grouped variables and individuals are available in Fig. 3 and Fig. 4 , respectively. These two dimensions reflect 24.8% of the whole dataset. The correlation circle in Fig. 3 a illustrates how the main quantitative variables contribute to the first two dimensions, while Fig. 3 b provides a synthetic comparison of the groups of variables. Furthermore, the variance analysis allows us to identify the qualitative categories, projected in Fig. 3 c, that best characterise the coordinates of the individuals on the first two dimensions. Amongst the 27 variables related to necropsy lesions used in the analysis, eight of them best characterised the individuals on the first two dimensions: ‘respiratory fibrin’, ‘liver fibrin’, ‘cardiovascular fibrin’, ‘liver vascular congestion’, ‘celomic cavity fibrin’, ‘kidney and ureters change in composition’, ‘respiratory vascular congestion’, ‘gastrointestinal change in composition. The contextual and aetiological variables were not used to construct the MFA and, therefore, are fully independent of the first two dimensions. However, ten of the 26 contextual and aetiological variables were correlated with the first two dimensions. These ten variables included four bacteria taxa (i.e., ‘ Staphylococcus spp .’, ‘ Ornithobacterium rhinotracheale’, ‘E. coli’ and ‘Riemerella anatipestifer’ ), two viral circulation variables (i.e., confirmed circulation of IBDV and aMPV), one Eimeria status (i.e. ‘High cecum infestation’) and two contextual variables (i.e., the flock’s region ‘PL9’ and ‘PL8a’). The correlation circle of the quantitative variable by themes (a), groups (theme) representation (b) and projection of the qualitative categories (supplementary in blue, active in red, categories link to an absence, meaning, lesions or bacteria not found in the flock, in grey) significantly associated with either the first or the second dimension (c). HC (Hierarchical clustering) A partition in three clusters was inferred from the minimum ratio between two successive within-group inertias and is illustrated in Fig. 4 . The clusters were named 1, 2 and 3 for ease, but a specific name was given based on the MFA active variables, which best characterised the group. All variables significantly characterising these groups are described in Table 8 , Table 9 , Table 10 and Table 11 based on the characteristic of the theme they refer to (active, supplementary, qualitative or quantitative). The results, including the non-significant variables, are available in the Additional file 3. Table 8 Mean and Standard deviation (SD) for each quantitative variable per cluster. Variable Overall Cluster 1 Cluster 2 Cluster 3 Mean SD Mean SD p V Mean SD p V Mean SD p V Production performance FCR 1.6 0.1 1.6 0.1 1.5 0.1 *** -6.0 1.7 0.1 *** 6.9 Mean age at slaughter 38.9 1.3 38.3 1.2 * -2.6 38.9 1.4 39.7 0.9 ** 2.9 Mean weight at slaughter 2.5 0.2 2.5 0.1 * -2.2 2.6 0.1 ** 3.0 2.5 0.2 Health performance Mortality 5.7 4.4 5.1 3.0 4.5 2.4 *** -3.3 9.8 7.1 *** 5.0 Condemnation 0.5 0.3 0.7 0.5 * 2.4 0.5 0.3 ** -3.1 0.6 0.3 Supplementary EPEF 392.4 45.3 387.6 37.7 415.5 27.7 *** 6.2 331.3 35.7 *** -7.1 FPLS 64.7 36.0 62.5 39.2 60.8 35.9 78.9 29.5 * 2.1 The variables came from quantitative themes set as active (‘Production performance’, ‘Health performance’) or supplementary (‘Economic indicator’) used in the MFA/HC and performed on 113 flocks for each cluster. For each cluster and quantitative variable, the results of the t-test comparing the cluster to the overall mean are gathered in the table with V for the value of the t-test and p for the p-value. Only significant variables are described, and p-values are coded using asterisks: * for p < 0.05, ** for p < 0.01, *** for p < 0.001. Table 9 Frequency and proportion of each variable of the active qualitative theme ‘Context’ used in the MFA/HC performed on 113 flocks and for each cluster. Variable Factor Overall Cluster 1 Cluster 2 Cluster 3 N (%) N (%) p V N (%) p V N (%) p V Context Region PL6 6 5.3 0 0 5 7.9 1 4.2 PL8a 25 22.1 2 7.7 * -2.1 18 28.6 5 20.8 PL8b 14 12.4 1 3.8 9 14.3 4 16.7 PL9 68 60.2 23 88.5 *** 3.5 31 49.2 ** -2.6 14 58.3 Farm type Large 65 57.5 9 34.6 ** -2.6 37 58.7 19 79.2 * 2.4 Small 48 42.5 17 65.4 ** 2.6 26 41.3 5 20.8 * -2.4 Season Automn 21 18.6 5 19.2 15 23.8 1 4.2 * -2.1 Spring 36 31.9 6 23.1 20 31.7 10 41.7 Summer 31 27.4 9 34.6 15 23.8 7 29.2 Winter 25 22.1 6 23.1 13 20.6 6 25 The variables are from the qualitative theme ‘Context’ used in the MFA/HC performed on 113 flocks. For each cluster and variable, the hypergeometric test results are gathered in the table with V for the sample estimate and p for the p-value. Only significant variables are described, and p-values are coded using asterisks: * for p < 0.05, ** for p < 0.01, *** for p < 0.001. Table 10 Frequency and proportion of each variable from the active qualitative theme ‘Necropsy lesions per cluster. Variable Factor Overall Cluster 1 Cluster 2 Cluster 3 N (%) N (%) p V N (%) p V N (%) p V Lesion (High confidence score) Cardiovascular - Fibrin No 86 76.1 2 7.4 *** -9.3 62 96.9 *** 6.0 22 100 ** 3.2 Yes 27 23.9 25 92.6 *** 9.3 2 3.1 *** -6.0 0 0 ** -3.2 Celomic cavity - Fibrin No 98 86.7 17 63.0 *** -3.7 61 95.3 ** 3.0 20 90.9 Yes 15 13.3 10 37.0 *** 3.7 3 4.7 ** -3.0 2 9.1 Kidney/ureters - Change in composition No 26 23.0 3 11.1 21 32.8 ** 2.8 2 9.1 Yes 87 77.0 24 88.9 43 67.2 ** -2.8 20 90.9 Kidney/ureters – Urates/ Uroliths No 22 19.5 1 3.7 * -2.5 13 20.3 8 36.4 * 2.1 Yes 91 80.5 26 96.3 * 2.5 51 79.7 14 63.6 * -2.1 Kidney/ureters - Vascular congestion No 74 65.5 22 81.5 * 2.0 42 65.6 10 45.5 * -2.1 Yes 39 34.5 5 18.5 * -2.0 22 34.4 12 54.5 * 2.1 Liver - Fibrin No 85 75.2 0 0 *** -10.4 63 98.4 *** 6.8 22 100 *** 3.3 Yes 28 24.8 27 100 *** 10.4 1 1.6 *** -6.8 0 0 *** -3.3 Musculoskeletal - Necrosis, ulcer No 9 8.0 2 7.4 2 3.1 * -2.1 5 22.7 * 2.4 Yes 104 92.0 25 92.6 62 96.9 * 2.1 17 77.3 * -2.4 Respiratory - Fibrin No 85 75.2 0 0 *** -10.4 63 98.4 *** 6.8 22 100 *** 3.3 Yes 28 24.8 27 100 *** 10.4 1 1.6 *** -6.8 0 0 *** -3.3 Lesion (Low confidence score) Cardiovascular - Thinning distended No 79 69.9 23 85.2 * 2.0 44 68.8 12 54.5 Yes 34 30.1 4 14.8 * -2.0 20 31.3 10 45.5 Respiratory -Vascular congestion No 24 21.2 10 37.0 * 2.2 12 18.8 2 9.1 Yes 89 78.8 17 63.0 * -2.2 52 81.3 20 90.9 The variables, from the qualitative theme ‘Necropsy lesions’ used in the MFA/HC performed on 113 flocks, are regrouped according to their confidence scores. For each cluster and variable, the hypergeometric test results are gathered in the table with V for the sample estimate and p for the p-value. Only significant variables are described, and p-values are coded using asterisks: * for p < 0.05, ** for p < 0.01, *** for p < 0.001. Table 11 Frequency and proportion of each variable of the supplementary qualitative theme per cluster. Variable Factor Overall Cluster 1 Cluster 2 Cluster 3 N (%) N (%) p V N (%) p V N (%) p V Bacteria status Bordetella avium No 100 88.5 26 96.3 53 82.8 * -2.1 21 95.5 100 88.5 Yes 13 11.5 1 3.7 11 17.2 * 2.1 1 4.5 13 11.5 Escherichia coli** No 45 39.8 5 18.5 ** -2.6 31 48.4 * 2.1 9 40.9 45 39.8 Yes 68 60.2 22 81.5 ** 2.6 33 51.6 * -2.1 13 59.1 68 60.2 Staphylococcus spp No 81 71.7 21 77.8 41 64.1 * -2.0 19 86.4 81 71.7 Yes 32 28.3 6 22.2 23 35.9 * 2.0 3 13.6 32 28.3 Eimeria status Meckel's diverticulum - ( E. maxima ) High 3 2.7 0 0 2 3.1 1 4.5 Low 15 13.3 3 11.1 6 9.4 6 27.3 No 95 84.1 24 88.9 56 87.5 15 68.2 * -2.1 Cecum (E. tenella ) High 12 10.6 1 3.7 5 7.8 6 27.3 * 2.5 Low 23 20.4 7 25.9 10 15.6 6 27.3 No 78 69.0 19 70.4 49 76.6 10 45.5 * -2.5 Rectum ( E. brunetti ) High 10 8.8 0 0 7 10.9 3 13.6 Low 17 15.0 7 25.9 5 7.8 * -2.4 5 22.7 No 86 76.1 20 74.1 52 81.3 14 63.6 Viral circulation aMPV No 44 38.9 16 59.3 * 2.4 24 37.5 4 18.2 * -2.2 SUS 46 40.7 9 33.3 26 40.6 11 50 Yes 23 20.4 2 7.4 14 21.9 7 31.8 The qualitative supplementary themes used in the MFA/HC performed on 113 flocks are ‘Bacteria status’, ‘Eimeria status’, and ‘Virus circulation’. For each cluster and variable, the hypergeometric test results are gathered in the table with V for the value of the test and p for the p-value. Only significant variables are described, and p-values are coded using asterisks: * for p < 0.05, ** for p < 0.01, *** for p < 0.001. Here add : Table 8 : Mean and Standard deviation (SD) for each quantitative variable per cluster. Table 9 : Frequency and proportion of each variable of the active qualitative theme ‘Context’ used in the MFA/HC performed on 113 flocks and for each cluster. Table 10 : Frequency and proportion of each variable from the active qualitative theme ‘Necropsy lesions per cluster. Table 11 : Frequency and proportion of each variable of the supplementary qualitative theme per cluster. Cluster 2 – High-performing flocks Cluster 2 included most of the flocks (n = 64) and can be defined as a cluster of high-performing flocks associated with a low ‘FCR’, high ‘EPEF’, and high ‘weight at slaughter’ (see Table 8 ). These flocks also had better health performances, including fewer necropsy lesions. This was particularly true for fibrinous lesions in the liver, cardiovascular and respiratory tract. However, ‘necrosis or ulcers of the musculoskeletal system’ were more common in this cluster than in the other. In terms of the presence of aetiological agent, Eimeria infestation, especially in the rectum, was less often observed, but two aetiological agents were over-represented, i.e., ‘ Bordetella avium’ and ‘Staphylococcus spp’ (Table 11 ). Cluster 3 – Low-performing flocks In opposition to cluster 2, cluster 3 (n = 22) can be defined as a cluster with flocks at an older age (longer production time) with low production and health performance raised in large farms (more than two chicken houses) (Table 8 ). Indeed, these flocks had a higher ‘FCR’, lower ‘EPEF’, and a higher ‘age at slaughter’, higher ‘mortality rate’ and higher ‘FPLS’ than the others. Particularly, one necrotic lesion was more frequent: ‘vascular congestion in the kidneys or ureters’, which has a high confidence score. Among the aetiological agents, the cluster showed evidence of infestation by Eimeria. Indeed, high infestations in the cecum or evidence of infestations in the Meckel’s diverticulum were observed more frequently in the cluster’s flocks. Furthermore, there were significantly more evidence of circulation (confirmed or suspected) of aMPV in this cluster than in the others. Cluster 1 – Flocks with fibrinous lesions Cluster 1 included 27 flocks and was characterised by birds slaughtered at a younger age and up to a lighter weight than the others. These flocks were generally raised on ‘small’ farms (less than two chicken houses), in the region ‘PL9’. They were more frequently associated with fibrinous lesions in the liver, cardiovascular, respiratory, and celomic cavity. ‘Uroliths were also observed more frequently in this cluster than in others. All these lesions were associated with a high confidence score, meaning they are likely antemortem lesions. In opposition to the two other clusters, this cluster presented no specificities regarding economic performance (‘EPEF’) but showed a higher condemnation rate. ‘ E. coli’ was the o nly aetiological agent significantly more present in this cluster. Discussion In our study, we investigated the relevance of endemic contagious diseases in Polish broiler flocks by exploring patterns in production and health performance variables, necropsy lesions, and the presence of aetiological pathogens. Indeed, the presence of an aetiological pathogen is not sufficient to understand the influence of a contagious endemic disease on production performances, especially when more than one pathogen circulates. The study population consist of 115 flocks held in Poland between 2022 and 2023. They are part of the larger Polish broiler industry, which has greatly improved its efficiency in the past 30 years, and is the main European producer of poultry meat[ 29 ]. More specifically, these flocks were produced just after the end of the COVID-19 pandemic during an avian flu epidemic, but in a period where low feed prices were prevalent, improving the Polish broiler production profitability [ 30 ]. In this context, the production performance of the study flocks was above the European average published by Van Limbergen et al. (2020), but lower than the Polish study case farm results presented by Adaszyńska-Skwirzyńska et al. (2025). Notably, they had an above-average cumulative mortality rate compared to both studies (6.01 ± 5.43 in the study flocks, compared to the European mean of 3.82% ± 3.70). The study flocks were not associated with any major health issues to our knowledge (such as an avian flu outbreak), but were subject to high infectious pressure, as illustrated in our study by the diversity of pathogens found in these flocks: all three viruses from the screening assay (IBV, aMPV, IBDV) were detected in the study’s population (based on serology, vaccination history and PCR tests), as well as thirteen different bacteria and Eimeria spp . These aetiological agents could potentially explain the above-average mortality rates observed, especially as they were associated with a diversity of necropsy lesions in the birds (such as necrosis or ulcer of the musculoskeletal system’, ‘uroliths in the ureters’ or ‘change in the composition (enlarged) of kidneys and/or ureters’). To go beyond this general overview, our study investigated whether patterns across the flocks in terms of production performance, health variables, and necropsy lesions could be observed in order to provide producers with a better understanding of their flocks' health. Using typologies to understand how the screened flock’s performance and diseases are associated Three flock types were identified using the HC analysis, each defined by specific characteristics in terms of production performance and presence of different pathogens. High performing flocks The high-performing flocks (cluster 2) show better health and production performances (Table 8 ) compared with the overall recorded flocks (Table 3 ), as well as with the European data published by Van Limbergen et al. (2020). Their production performances are close to Aviagen's 2022 performance objectives [ 31 ], which are defined as the breed’s attainable standards, for a ‘mean age at slaughter’ of 39 days (i.e., FCR of 1.5 ± 0.1 and 1.47 and average ‘weight at slaughter’ of 2.6 ± 0.1 kg and 2.7 kg for the high-performing flocks and Aviagen standards, respectively). These results highlight the strong production performance capacities of the studied farms. However, even if the cluster’s mean mortality (4.5% ± 2.4) was the lowest identified in our study, it remains higher than the reported European mean of 3.82% ± 3.70 [ 5 ], illustrating the potential for improvements in production performance. The reason for the relatively high mortality in these high-performing flocks is unclear, but it could indicate that the infectious pressure observed among all screened flocks also impacts, in part, those flocks, despite their good production performances. However, issues arising from rapid growth or other environmental factors cannot be ruled out. Two other characteristics defined those high-performing flocks. First, 96.9% of them presented ‘Musculoskeletal necrosis or ulcer’ lesions, the majority of which were necrosis of the femoral head (result not presented). These lesions are usually associated with higher body weight and increased stocking density of birds [ 32 , 33 ]. The ‘mean body weight at slaughter’ of the birds in the cluster is indeed slightly but significantly higher than the screened flocks (i.e., 2.6 kg and 2.5 kg, respectively). However, no information on their stocking density was available. These lesions are a common indicator of welfare issues in broiler intensive production. Furthermore, if these lesions develop spontaneously, ‘ Staphylococcus spp.’ is one of the main bacterial taxa associated with these lesions during secondary infection processes [ 32 , 34 ]. These bacteria were also overrepresented in this cluster. Exploring the practices in place in the Polish production context further could potentially help define sustainable and adaptable solutions for producers to mitigate this welfare issue without compromising current productivity levels. Second, ‘ Bordetella avium’ was more frequently present in these flocks than in the others. This bacterium causes Turkey coryza, but its pathogenicity is considered opportunistic in broilers [ 35 ]. Its presence is not known to impact production performance. Hence, the presence of Bordetella avium in 11 of the high-performing flocks does not suggest any pathogenicity as well. Low performing flocks The low-performing flocks (cluster 3) exhibit lower production and health performance, which can be put into perspective with the presence of two aetiological agents commonly observed in these flocks. The first one, Eimeria spp ., has been estimated to reduce a production performance indicator, to increase ‘FCR’ and increase ‘FPLS’ [ 36 , 37 ] as observed in cluster 3. However, estimating the impact of coccidiosis on production performance remains complex, especially in subclinical cases, as it is multifactorial, and co-infection with bacteria such as Clostridium Perfringens creates significant variation in production performance [ 38 ]. If causality between low production performance and coccidiosis cannot be assumed in this study, Eimeria spp . remains a major issue in broiler production, especially as pressure to reduce the use of coccidiostats (antimicrobial compounds) is growing [ 39 , 40 ]. Unfortunately, the lack of information regarding coccidiosis management practices does not allow this study to make any further conclusions. The other more frequent aetiologic agent present in cluster 3 was aMPV. This observation was made taking into consideration serology and vaccine history, meaning that identification of vaccine strains is not impossible but limited. Furthermore, in the past ten years, the burden of this respiratory pathogen on European broiler flocks has been rising [ 41 – 43 ], while diverse aMPV subtype B strains are being reported across Europe, with significant diversity compared to the initial strains introduced in France in 1985 [ 42 , 44 ]. Recent observations in Poland (Śmiałek 2024, unpublished) suggest that aMPV infections have more significant health impacts at the end of the production, surpassing the impact observed with either IBV and IBDV. Our study shows an association between poor production performances and the presence of aMPV. Therefore, it provides an additional incentive to investigate further aMPV and its potential impact on broilers at the end of their production cycle. In the meantime, vaccination remains the main aMPV control measure [ 41 , 42 ]. Flocks with fibrinous lesions The last type of flocks (cluster 1) shows high frequencies of fibrinous lesions at necropsy and a higher condemnation rate, suggesting a probable sub-acute/chronic contagious disease affecting the flocks at the end of production [ 45 ]. Despite the observed evidence of infection, these flocks did not perform worse than the average flock, especially on the economic ‘EPEF’ indicator. However, they were generally slaughtered at a smaller weight and a younger age. This could be due to the common practice of sending chickens to the slaughterhouse earlier than planned when clinical symptoms start to appear, to minimise potential losses. Furthermore, fewer treatments, especially antibiotics, are available for use due to the withdrawal period for human consumption, pushing producers to slaughter earlier to maximise their benefits instead of attempting treatment. The only aetiological agent associated with this cluster was avian pathogenic E. coli. The necropsy results in the cluster’s flocks are also consistent with literature reports about colibacillosis, even if the lesions are not specific: perihepatitis, airsacculitis, pericarditis and peritonitis [ 46 , 47 ]. Colibacillosis is also known as a major cause of carcass condemnation [ 47 ], which is consistent with the observed high rate of condemnation in the cluster. Furthermore, the infection can be primary, but is more often considered secondary [ 47 , 48 ]. Our study did not identify other aetiological agents associated with the high prevalence of E. coli. Indeed, as sampling only occurred once at the end of production, this might have been too late for the primary causes of E. coli to be identified. Further work would be needed to study the potential primary source of infection or predisposing factors. To initiate the investigation, characteristics of the cluster can be utilised. For example, the flocks in this cluster were most frequently associated with the region (PL9), which is characterised by a higher density of poultry production (Smialek 2025, unpublished data). The burden of a diverse set of endemic contagious diseases in highly dense areas has been shown to play a role [ 49 ]. Their single or grouped contribution to the performances of these flocks should be further investigated, especially as our study did not identify a specific aetiological agent more frequently present in these flocks. Moreover, environmental issues such as inadequate ventilation and poor hygiene practices have been identified as predisposing factors and should not be dismissed [ 48 ]. Beyond the clusters Four additional aetiological agents were associated with the first two dimensions of the MFA reflection, 24.8% of the dataset’s variability, and can provide further insight into the health and performance of our study population. Other aetiologic agents associated with specific performance and necropsy profiles Among the aetiological agents identified as significant in the MFA, one virus (IBDV) and two bacteria taxa (‘ Ornithobacterium rhinotracheale’ and ‘Riemerella anatipestifer’ ) do not appear in the clustering analysis, which can be used to draw further conclusions from our study. All of those aetiological agents were pathogens of poultry flocks (i.e., ‘IBDV’, ‘ Ornithobacterium rhinotracheale’ , and ‘ Riemerella anatipestifer’ ) associated with low production and health performances (see Fig. 3 c). For example, the only two flocks infected with IBDV despite vaccination exhibited low production and health performance, which is consistent with the immunosuppressive effects of IBDV infections [ 50 ]. This observation remains limited to two flocks, and to draw any conclusions, it would be necessary to know if more similar observations were made in other flocks. If the answer is yes, then it raises questions about the current vaccination protocol: are they properly carried out or could the vaccination failure be due to the presence of new IBDV strains circulating in Central Europe [ 51 , 52 ]. Concerning flocks with ‘ Ornithobacterium rhinotracheale’ , they were characterised by the presence of fibrinous lesions and poor health performance (see Fig. 3 c), which is expected for a respiratory pathogen observed in secondary infections in broilers [ 53 ]. Similarly, Riemerella anatipestifer is a respiratory pathogen that is scarcely described in broilers in the literature, but is more commonly described in duck or turkey flocks [ 54 , 55 ]. In our study, we observed few cases of flocks infested with ‘ Riemerella anatipestifer’ (7%, 8 flocks). Furthermore, in the MFA, the bacterial taxa had close coordinates to ‘ Ornithobacterium rhinotracheale’ . Indeed, infection caused by both bacteria taxa in broilers can be misdiagnosed before laboratory testing as one another or as other aetiologic agents. Riemerella anatipestifer ’s presence in flocks supports the need to investigate further the impact of this disease on Polish broiler production and raises questions about the route of infection, illustrating the importance of biosecurity and good hygiene practices in dense multi-species poultry production regions [ 55 ]. Prevalent pathogen and lesions not identified in the analysis IBV was the virus with the most flocks with evidence of circulation (after treatment of vaccine history, serology and RT-PCR results) in our study population (42% of the study flock), but was not associated with any flock types or MFA dimensions. This is surprising because its health burden is reported to be one of the heaviest on the poultry industry, including in Poland [ 56 , 57 ]. Hence, this observation could be due to the isolation of vaccine strains; however, the use of serology and vaccine information limits this hypothesis. On the other hand, Legnardi et al. (2019) investigated IBV circulation in Poland and reported the absence of systemic clinical signs in flocks despite evidence of virus circulation. They concluded that the widespread vaccination in Poland is effective in reducing the current IBV burden. Similar observations from Italy and France were reported [ 58 – 60 ]. This could explain why we did not observe a relation between the presence of IBV and our production and health variables, indicating that despite its wide presence in the population, the virus is not affecting health and production performance. Legnardi et al. (2019) also described a specificity concerning the Polish poultry industry, where a large number of IBV vaccination programs were being followed without any clear rationale. During this study, we observed similar practices: 25 different vaccination programs were used across the 115 flocks from 59 different farms (results not shown). This practice increases the risk of vaccine virulence reversion and could play a role in the future emergence of new IBV strains [ 61 ]. Beyond the impact of IBV on flocks, our study re-highlights the need for timely contextual information to move towards a more rational approach towards IBV vaccination management. In addition to aetiological agents, two major necrosis lesions in the urinary tract were identified at unexpected frequency: the presence of uroliths (80%) and ‘change in the composition (enlarged) of kidneys and/or ureters’ (76%). Neither lesion was associated with any flock type, providing little additional information. In this context, further research is needed to understand their cause/origin better. Challenges and limitations of data re-use In this study, routinely collected data from farmers and veterinary laboratories were reused for research purposes, providing insight into the health and production performance of broiler flocks in Poland. However, reusing this available data for research is associated with some limitations. First, the flocks included in the analysis were not randomly selected. Our results confirmed that the production and health performance of screened flocks were not representative of the entire study population. Producers may have preferentially selected flocks with poorer performances for screening. Therefore, any generalisation of results should be done with care, taking into consideration these selection biases. Second, data on farm management, nutrition and environmental factors were not available, which limited the extent of our analysis, as illustrated, for example, by the absence of information on coccidiosis management practices, stocking density, or production environmental status (such as ventilation and temperature). Furthermore, it was the first time that these farms and the laboratory had made their data available to an external researcher for digital reuse at this scale. To do so, in the absence of a comprehensive automated data management process, they manually manipulated the data by integrating multiple spreadsheets or transcribing PDFs into spreadsheets. Such processes are known to create errors in the data. To mitigate this issue for the transcribed data, any discrepancies identified were sent to the laboratory for correction and validation. The next steps of data integration and preparation for analysis were also automated to minimise further errors. Fully estimating the impact of such data errors is not possible. Additionally, the fact that these data were routinely collected for production purposes is associated with some limitations. First, the laboratory techniques used were defined to provide an operational screening for the farmer and their veterinarian, specific to their context, and not to be compared with other research results that answer specific research questions. For example, in studies examining the presence of IBV, techniques based on the complete sequencing of the S1 gene (the region where most of the IBV genetic variability is concentrated) are used, but these techniques are more expensive, more time-consuming and harder to implement [ 62 ]. Currently, they cannot be used for routine field screening by the Polish veterinary laboratory. Similarly, production performance data were created primarily to fulfil the industry’s needs. For example, ‘mortality’ is calculated by farms as the number of chickens sold minus the number of chicks and is mainly used to calculate the flock's economic benefit. A few ‘recorded’ flocks showed a negative ‘mortality’ (Table 3 ), illustrating the fact that this value is an estimation and not the true ‘cumulative mortality’. Indeed, it assumes that the no chicken movement occurred, which is indeed rare, and that the number of chicks ordered at the hatchery is the number delivered, which is often not true. Indeed, hatcheries can send off additional chicks to the order to anticipate any loss due to transport or for other commercial reasons, meaning that mortality in our study can be underestimated. However, considering that the mortality observed was generally high, the impact of this bias on our observation is expected to be limited. Untapped potential of data re-use Reusing routinely collected data on animal health to enhance contextual knowledge of endemic contagious diseases has been promoted to increase coverage and timely data collection in ways that traditional data collection through research surveys cannot [ 7 , 63 ]. However, we found only a few examples of similar work that integrated at least two different data sources and did not rely on an additional farm survey to fulfil the research goal [ 7 , 64 , 65 ]. As described above, our study lacked the coverage typically associated with reusing routinely collected data and was subject to the biases common to data reuse. Despite these limitations, due to the diversity of the collected variables, the study was able to identify and formulate multiple hypotheses to support future design studies, which could later be used to provide recommendations to producers. The study also serves as a practical example of the value of data reuse for health management, providing a multivariate description of flock health and demonstrating the importance of investing in improved data management and collection systems for the industry. This is even more essential when investment in integrated health data systems is known to be costly, especially for small-scale farms, such as many of them in Poland [ 10 , 17 , 63 ]. By illustrating the value of data routinely collected by the industry for generating new knowledge, our study therefore advocates for better and wider access to such data by creating systems and data flows that integrate relevant information to generate animal health intelligence [ 13 , 9 , 10 , 63 ]. Conclusion The study enabled the identification of three groups of flocks defined by specific performance status and necropsy lesions associated with known pathogens relevant to Polish broiler production, by reusing and integrating data regularly produced by the industry. As such, it demonstrates the value of this data in enhancing the monitoring and understanding of endemic contagious diseases and their interactions within a specific context. This study provides an example of how such data can be used to provide farmers and veterinarians with a deeper understanding of the primary characteristics and disease issues affecting their flocks, offering contextual information and hypotheses to enhance their disease prevention and management efforts. Declarations Author contribution C.D. cleaned and integrated the data shared by data owners with the support of M.S., who communicated with the data owners. C.D. and C.F. designed and performed the study. C.D. carried out the analysis with input from C.F. for statistical analysis and from M.S. and J.J.W. for interpretation of laboratory results. C.D. and C.F. wrote the manuscript with feedback and support from M.S. and J.J.W. All authors read and approved the final manuscript. Ethics approval and consent to participate: Not applicable. The study reuses data that were originally produced for another purpose. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Acknowledgement: We want to thank SLW Biolab and the broiler producers for sharing the data for this study. Specifically, we would like to extend special thanks to Marta Gańko, Ilona Czokajło, and Karolina Kowalewska, who collected the data across all the laboratory systems and answered our questions about it. We would also like to thank the Royal GD pathologists who shared their confidence level in the different necropsy lesions available in the data. Funding: This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101000494 (DECIDE). Data Availability: The data analysed during the current study are available in the Zenodo repository, DOI: 10.5281/zenodo.17406208. Code availability: The code supporting this article's conclusion is available in a Zenodo repository linked to a public GitHub repository, DOI: 10.5281/zenodo.17424234. References FAO. World Agriculture towards 2030/2050: the 2012 revision. 2012. https://www.fao.org/3/ap106e/ap106e.pdf . Accessed 18 Apr 2024. Oviedo-Rondón EO. Optimizing the health of broilers. Burleigh Dodds Ser Agric Sci. 2022. https://doi.org/10.19103/AS.2022.0104.11 . Franzo G, Legnardi M, Faustini G, Tucciarone CM, Cecchinato M. Animals. 2023;13:1804. https://doi.org/10.3390/ani13111804 . When Everything Becomes Bigger: Big Data for Big Poultry Production. 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07:14:36","extension":"html","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":321050,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7939430/v1/e3ac44296c13534e060ad60c.html"},{"id":95895683,"identity":"0d12eadc-7a56-4f1a-b263-11e0bc96c7d2","added_by":"auto","created_at":"2025-11-14 07:14:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83223,"visible":true,"origin":"","legend":"\u003cp\u003eData cleaning and integration process. Both datasets were made of multiple Excel spreadsheets. The data was processed and integrated following the process described in Figure 1 and performed using R [18]. The datasets were combined using the chicken house ID, flock production date, and laboratory analysis date. The integrated dataset comprised 115 flocks, also called the ‘screened flocks’ in the rest of the paper.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7939430/v1/b56a6f85308e43ebee125108.png"},{"id":95895686,"identity":"01cae003-a32e-4b12-b163-6dc1e1d3cbbe","added_by":"auto","created_at":"2025-11-14 07:14:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":130443,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix between all quantitative variables based on the information collected.\u003c/p\u003e\n\u003cp\u003eFigure A contains the screened flocks (n = 115); in Figure B, recorded flocks were used for the analysis (n = 1697).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7939430/v1/1c757e9f7c9efdb5e45ed944.png"},{"id":96242831,"identity":"23d91502-3355-4182-a4ad-3d97971298e4","added_by":"auto","created_at":"2025-11-19 07:14:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":180686,"visible":true,"origin":"","legend":"\u003cp\u003eMain results of the MFA\u003c/p\u003e\n\u003cp\u003eThe correlation circle of the quantitative variable by themes (a), groups (theme) representation (b) and projection of the qualitative categories (supplementary in blue, active in red, categories link to an absence, meaning, lesions or bacteria not found in the flock, in grey) significantly associated with either the first or the second dimension (c).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7939430/v1/f07845d928f4abf04cb997c1.png"},{"id":96242952,"identity":"e3e1220e-b650-4905-8324-2cd6db2a3830","added_by":"auto","created_at":"2025-11-19 07:15:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":168305,"visible":true,"origin":"","legend":"\u003cp\u003eFlock projection in the MFA’s first two dimensions coloured by clusters and hierarchical tree.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7939430/v1/4821fbbe60016f34ecdc3c7f.png"},{"id":104739484,"identity":"ffe69e3e-c6a4-4713-9301-8248345b4f8a","added_by":"auto","created_at":"2026-03-16 16:07:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2553089,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7939430/v1/6d3234df-da83-4376-b418-98cf7ad2e0c9.pdf"},{"id":95895682,"identity":"d9c47016-c955-4d67-847a-ff08fc05ccc4","added_by":"auto","created_at":"2025-11-14 07:14:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":42060,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7939430/v1/cc9997b38d40a7944a1871a4.docx"},{"id":96243848,"identity":"80be6db0-b6c0-4832-992a-270a0b672fd2","added_by":"auto","created_at":"2025-11-19 07:17:09","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29103,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7939430/v1/165f9bded90d4b3acc45c334.xlsx"},{"id":96242277,"identity":"b064db36-69b1-494b-9bf4-b08597ca6e9d","added_by":"auto","created_at":"2025-11-19 07:12:30","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":92990,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7939430/v1/29ff8c6cf88dbb99d9e78781.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating heterogeneous data to address endemic diseases in broiler production: insights from a Polish case study","fulltext":[{"header":"Background","content":"\u003cp\u003eSince the 1970s, the global poultry industry has expanded continuously to meet a growing demand for poultry meat [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This production growth could not have been achieved without a steep increase in production efficiency due to growing technical capacities in genetics, housing, nutrition, biosecurity and health management [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite these achievements, the broiler production sector still faces multiple challenges associated with increased resource competitiveness, growing threats of emerging infectious diseases, and higher consumer expectations concerning food safety, animal welfare, and environmental impacts [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Addressing these challenges requires the industry to evolve while maintaining low production costs to meet market demand. One of the main areas of improvement for increasing production efficiency lies in mitigating the impact of endemic contagious diseases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, these diseases are multifaceted and complex, involving interactions between pathogens, animal genetics, and the environment, varying their impacts across the diversity of contexts [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This complexity hinders the accessibility of up-to-date information regarding the status and production impact of poultry endemic contagious diseases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], despite their necessity to address the sector’s persistent challenges.\u003c/p\u003e\u003cp\u003eImplementing epidemiological studies on endemic contagious diseases and their impacts can be expensive, time-consuming, and often too slow to keep pace with context-specific trends [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Using pre-existing data sources to generate more timely and targeted information for the industry has been described as a cost-effective solution compared to some traditional approaches [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In the broiler production sector, it has been observed that data collection and digitalisation have also escalated with the expansion of the technical capacities. Indeed, stakeholders across the value chain increasingly use data to support their activities with different intensity levels [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These data offer timely insight into farm and poultry production, as well as the activities of other value chain stakeholders, which could be key to optimising endemic contagious disease management [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Specifically, integrating production and health data could be particularly valuable to inform on the main endemic contagious disease burden, providing data-driven information to prioritise investments and action [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eData health solutions are being developed in precision livestock farming to meet this opportunity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, they mainly concentrate on gathering new data rather than integrating existing data sources [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These approaches thus do not address the specific issue encountered by poultry producers, where data about a flock can be dispersed across multiple actors throughout the production chain (e.g., flock manager, slaughterhouse, feed mill, veterinarian, diagnostic laboratory, and competent governmental authorities), even in the context of highly integrated production systems. If integrating these data holds multiple promises to support the industry in optimising broiler health, it is also associated with challenges regarding data ownership, accessibility, and integration [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, more generally, farmers’ adoption of digital technologies has been hindered by high cost, lack of comprehensive technological offer and trust that these technologies will not cause them harm, requiring strong collaboration to bridge the gap [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Before significantly investing in data integration, researchers need to demonstrate to producers the value of data integration in terms of support for production and health management [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePoland is Europe's largest poultry meat producer, with 2,746 thousand tonnes of meat produced in 2023 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Polish producers suffer the same constraints as the rest of the industry, but operate in an environment characterised by rising production costs and overall volatility of raw material prices [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, the Polish broiler production is not highly integrated despite its size [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This fragmented structure fosters a highly competitive environment, breeding distrust and hindering the collection and integration of data across the value chain. In this context, comprehensive information about poultry endemic contagious diseases in Poland, such as coccidiosis, infectious bronchitis virus (IBV), infectious bursal disease virus (IBDV), or avian metapneumovirus (aMPV), is scarce.\u003c/p\u003e\u003cp\u003eThe present study aims to improve our understanding of the major endemic contagious diseases affecting the Polish production system by reusing high-dimensional data from broiler producers and a diagnostic laboratory. To achieve this, the study identifies the predominant health and production patterns amongst the studied broiler flocks using clustering techniques. It discusses their potential association with specific aetiological agents to define strategies to enhance animal production, health, and welfare. Furthermore, it assesses the quality of the information produced. Finally, it discusses its application to Polish broiler stakeholders in a context where data integration and reuse for health management purposes are relatively uncommon.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eData sources\u003c/p\u003e\u003cp\u003eAll the data came from pseudo-anonymised poultry farms growing Ross308 broilers in the northeast region of Poland between 2018 and 2023. They were made of two distinct datasets: the “chicken farm” dataset provided by poultry producers, and the “laboratory” datasets provided by a veterinary diagnostic laboratory (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe “chicken farm” dataset included standard indicators used to evaluate performance of broiler flocks (i.e., mortality, death on arrival at the slaughterhouse, mean age at slaughter, foot-pad lesion scores, etc.) and information about vaccinations and antibiotic treatments administered during the flock production cycle (i.e., name of the product or active substance, day of treatment, and route of administration). This dataset collected information from 59 farms, but the amount of information available for each farm varied between one and 31 production cycles. Moreover, the treatment data was available for less than 7% of the flocks with production performance data.\u003c/p\u003e\u003cp\u003eThe “laboratory” dataset was related to tests performed during the last week of production cycles as part of a screening program proposed to farmers by the veterinary diagnostic laboratory. This dataset collected information from 183 flocks coming from 98 farms. Necropsy observations and bacteriological, parasitological, and virological test results were available for five chickens randomly sampled per flock. On the same day, blood/serum from 23 birds of each screened flock was also randomly collected to be analysed serologically for three diseases (the same as the one investigated in the virological test). Technical details on the laboratory test used are available in Additional file 1.\u003c/p\u003e\u003cp\u003eData processing\u003c/p\u003e\u003cp\u003eThe data was then processed into 78 variables grouped into eight themes. Half of the themes were related to the data provided by the broiler producers (i.e., ‘Context’, ‘Economic indicator’, ‘Production performance’, ‘Health and Welfare’), three to the data provided by the laboratory (i.e., ‘Necropsy lesions’, ‘Bacteria status’, ‘Eimeria status’) and one (i.e. ‘Viral status’) was based on a combination of information extracted from the two datasets. The themes and their associated variables are described below and summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eDescription of the integrated data after cleaning and processing into eight themes and 73 variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThemes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eType*\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDefinition\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eContextual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProduction mainly occurred from the 1st of March until the 31st of May\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProduction mainly occurred from the 1st of June until the 31st of August\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAutumn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProduction mainly occurred from the 1st of September until the 30th of November\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWinter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProduction mainly occurred from the 1st of December until the 28th of February\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePL9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFarm’s postal code was included in region PL9 -Masovian voivodeship\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePL6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFarm’s postal code was included in region PL6- Północny region\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePL8a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFarm’s postal code was included in southern region PL8 - Lublin Voivodeship\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePL8b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFarm’s postal code was included in northern region PL8 - Podlaskie Voivodeship\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarm size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFarm with 1 or 2 chicken houses\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLarge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFarm with over two chicken houses\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATB use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAntibiotics were used during the production cycle\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAntibiotics were used during the production cycle\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEPEF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eContinuous\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEuropean production efficiency factor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eProduction performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean age at slaughter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eContinuous\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean age at slaughter of birds in days\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean weight at slaughter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eContinuous\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean measured weight of birds slaughtered in Kg\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFCR (Feed conversion rate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eContinuous\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal weight of feed distributed to the flock divided by the total weight of birds sold\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eHealth and welfare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInterval\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of chicks ordered at the hatchery minus the number of birds sold to the slaughterhouse divided by the number of birds sold (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDOA (Dead on arrival)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInterval\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of birds dead on arrival (transport) divided by the number of birds sold (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCondemnation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInterval\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of birds condemned divided by the number of birds sold (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFPLS\u003c/p\u003e\u003cp\u003e(Foot-pad lesion score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInterval\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSum of the weighted product of birds with foot-pad lesions of grade 1 (*0.5) and grade 2 (* 2) amongst a hundred randomly sampled birds\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNecropsy lesion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e40 variables listed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe validated lesion wasn’t observed in the screened flocks\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe validated lesion was observed in at least one screened flock\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBacteria status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e19 variables listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe bacteria group wasn’t identified in the screened flocks\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe bacteria group was identified in at least one of the screened flocks\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEimeria status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariable defined for 4 intestinal sections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo evidence of circulation - the sum of all grades of screened birds equals to 0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow evidence of circulation - the sum of all grades of screened birds is between 1 and 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh evidence of circulation- the sum of all grades of screened birds is over 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eVirus circulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eVariable defined for 3 endemic viral diseases.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo virus field strain circulation; see Additional file 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSuspicion of any virus field strain circulation, see Additional file 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHSUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh suspicion of IBV virus field strain circulation; see Additional file 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConfirmed field strain circulation; see Additional file 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eHere add :\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cem\u003eDescription of the integrated data after cleaning and processing into eight themes and 73 variables.\u003c/em\u003e\u003c/p\u003e\u003ch3\u003eTheme: Context\u003c/h3\u003e\u003cp\u003eFour contextual variables were defined: ‘season’ and ‘region’ of production, ‘farm size’ and Antibiotic use (‘ATB use’). The season of production was determined based on the dates of flock production. A four-season model was used, with spring beginning on March 1st, summer on June 1st, autumn on September 1st, and winter on December 1st. Flocks with a production period overlapping two different seasons were assigned to the season that overlapped the most with the production period (most days). The production region was based on postal codes and the definition of the major socio-economic regions as defined in the European nomenclature of territorial units for statistics (NUTS 1), except for one, the NUTS coded ‘PL8’, which was divided into two parts: north and south. The farm size was defined based on the number of chicken houses [1–2, or over 2]. Finally, flocks were categorised based on the use or non-use of antibiotics during the production cycle.\u003c/p\u003e\u003ch2\u003eTheme: Production performance\u003c/h2\u003e\u003cp\u003eThree flock performance variables were identified and based on existing indicators which had been defined and used by the poultry farmer to monitor their flocks: ‘mean age at slaughter’ in days, ‘mean weight at slaughter’ in Kg and the ‘Feed Conversion Rate’ (‘FCR’). The ‘mean age at slaughter’ and ‘mean weight at slaughter’ were defined in the dataset and, in the usual broiler value chain, are calculated by the slaughterhouse as the geometric mean of all broilers' age and weight sent to the slaughterhouse during the flock’s thinning and final clearance. The ‘FCR’ is a standard indicator calculated based on available information in the dataset and is defined as the total weight of feed (Kg) provided to the flocks divided by the total weight of birds sold at the slaughterhouse.\u003c/p\u003e\u003ch3\u003eTheme: Health and welfare\u003c/h3\u003e\u003cp\u003eFour health and welfare variables were defined. ‘Mortality’ was expressed as a percentage and defined as the difference between the number of chicks bought at the hatchery and the number of birds sold at slaughter (before death in transport and condemnation) divided by the number of birds sold at slaughter. The ‘DOA’ (Dead on arrival) percentage was defined as the number of birds dead on arrival at slaughter (during transport) divided by the total of birds sold at slaughter. The percentage of ‘condemnation’ was defined as the number of birds condemned at the slaughterhouse divided by the number of birds sold to the slaughterhouse. The ‘FPLS’ (Foot-pad lesion score) variable of each flock is a weighted indicator based on the individual foot-pad lesion scores of a hundred birds sampled at the slaughterhouse, as required by the EU regulation\u003csup\u003e1\u003c/sup\u003e. Each bird is given a foot-pad lesion score: ‘0’ if no lesions were found, ‘1’ if the lesions were not too extensive and ‘2’ if they were. Then, the foot-pad lesion score of the flock is calculated by summing two products: 0.5 times the number of birds with a foot-pad lesion of grade ‘1’ and two times the number of birds with a foot-pad lesion of grade ‘2’.\u003c/p\u003e\u003ch3\u003eTheme: Economic indicator\u003c/h3\u003e\u003cp\u003eBroiler producers use the European Production Efficiency Factor (EPEF) indicator to compare flocks. This indicator is calculated based on the others presented above: ‘mortality’, ‘FCR’, ‘mean age at slaughter’ and ‘mean weight at slaughter’. It’s the ‘mean weight at slaughter’ times the survival rate (a hundred minus the ‘mortality’) divided by the product of the ‘FCR’, ‘mean day of slaughter’ and a constant of 0.1 to adjust for units.\u003c/p\u003e\u003ch3\u003eTheme: Necropsy lesions\u003c/h3\u003e\u003cp\u003eThirty-five variables linked to necropsy investigations were identified. A list of 115 different necropsy lesions was available in the raw data. These lesions were grouped into nine anatomic systems or organs (e.g., cardiovascular system, spleen) and thirteen lesion types (e.g., fibrinous lesions, changes in the organ structure). Each group of lesions was turned into a binary variable coded as follows: ‘Y’ if at least one lesion from that group was observed in at least one of the five screened flocks, and ‘N’ if no lesion was observed in the screened flocks. The 40 groups are described in detail in Additional file 2. Furthermore, for each observed lesion, a confidence score was defined by pathologists based on their confidence in the fact that the lesion was present in the animal before collection or, rather, resulted in post-mortem deterioration of the animal. The scale was defined as follow: ‘high’ means pathologists were confident that the lesion existed antemortem, ‘low’ means pathologists had a low confidence that this lesion existed antemortem, ‘very Low’ means pathologists believed that the lesions did not exist antemortem and were rather due to post-mortem deterioration of the animal. Only the 18 lesions associated with a confidence score of ‘high’ and ‘low’ were retained for the analysis.\u003c/p\u003e\u003ch3\u003eTheme: Bacteria status\u003c/h3\u003e\u003cp\u003eNineteen variables related to the bacterial status of the flocks were built using 41 different bacteria taxa available in the raw data. These bacteria taxa could correspond to varying levels of classification of bacteria from the most detailed (ie, a bacteria species subtype) to the least (ie, bacteria genus). If a high classification level (such as genus) was used, all sub-levels were classified using the lowest common level (in this case, the genus). The following information was available for each screened flock: the observed bacteria taxa, the number of screened birds with samples from necropsy lesions where the bacteria grew, the observed bacteria growth intensity, and the sample's localisation in which the bacteria were identified (see Additional file 2 for more information). In the case of ‘\u003cem\u003eEscherichia coli’ (E. coli) and\u003c/em\u003e ‘\u003cem\u003eClostridium perfringens’\u003c/em\u003e, due to their ubiquity and the high probability of cross-contamination, only samples from internal organs (heart, liver, spleen and bone marrow) were kept for the analysis. Then, only when the growth intensity was over one in these samples was the result interpreted as the true presence of either bacteria. Finally, each variable was defined as a binary variable where ’Y’ means at least one of the bacteria from that taxa was identified in at least one of the birds of the screened flock, and ‘N’ means no bacteria taxa were found in the screened flock.\u003c/p\u003e\u003ch2\u003eTheme: Eimeria status\u003c/h2\u003e\u003cp\u003eFour variables related to the Eimeria status were defined based on the Eimeria species' preferred tissue tropism in the intestines. Indeed, Eimeria species present tissue tropism, meaning that parasite localisation is usually associated with a specific Eimeria species: \u003cem\u003eE. acervulina\u003c/em\u003e with the duodenal loop, \u003cem\u003eE. maxima\u003c/em\u003e with the Meckel’s diverticulum, \u003cem\u003eE. tenella\u003c/em\u003e with the cecum and \u003cem\u003eE. brunetti\u003c/em\u003e with the rectum [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the raw data, each section of the intestines of each screened bird was given an infestation grade. This grade was defined at ‘0’ when no oocysts were found in the specific intestinal section, ‘1’ when 1 to 9 oocysts were observed, ‘2’ when 10 to 50 oocysts were observed, ‘3’ when more than 50 oocysts were observed. Each Emeria variable was turned into a categorical variable made of three levels defined according to the sum of the grade of Eimeria infestation across all five necropsied chickens: ‘no infestation’ if the sum equals 0, ‘low infestation’ if the sum is between one and three, ‘high infestation’ if the sum is above three.\u003c/p\u003e\u003ch3\u003eTheme: Viral circulation\u003c/h3\u003e\u003cp\u003eOne variable was created for each of the three endemic viruses considered important for poultry producers: infectious bronchitis virus (IBV), infectious bursal disease virus (IBDV), and avian metapneumovirus (aMPV). Virus circulation was estimated based on information available on vaccine type application, geometric mean titres of the serological samples, and RT-PCR results. Four categories for virus circulation were defined: ‘no’ if no virus circulation, ‘SUS’ if virus circulation is suspected, ‘HSUS’ if the virus circulation is highly suspected (category only defined to quantify IBV field circulation), and ‘yes’ if the virus circulation is confirmed. The definitions of these categories for each virus are available in Additional file 2.\u003c/p\u003e\u003cp\u003eData analyses\u003c/p\u003e\u003ch3\u003eData description\u003c/h3\u003e\u003cp\u003eAll data processing and calculations were performed using R (v.4.1.1) software [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The production performances and health characteristics of the screened flocks were described, and outliers, uncorrelated variables and rare health events were identified. The correlation between quantitative variables was evaluated using Spearman correlation. The representativity of the screened flocks compared to other flocks was assessed by comparing the mean production performances of the screened flocks with the ones from the recorded flocks using a two-tailed Welch's two-sample t-test.\u003c/p\u003e\u003ch2\u003eMultifactor analysis and Hierarchical Clustering\u003c/h2\u003e\u003cp\u003eMultifactor analysis (MFA) is a form of exploratory dimensional multivariate analysis that explores the relationship between quantitative and/or qualitative mixed variables by reducing the data to a few dimension that retains most of the data variability. It was first performed to identify the links between the variables and groups of variables and was then followed by a hierarchical clustering (HC) analysis with k-means consolidation. The HC is a means to visualise the variability and similarities between individuals (i.e., flocks) regularly used in veterinary epidemiology [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Using MFA before HC allows the compilation of the Euclidean distance between groups, individuals, and variables and is used to reduce the number of variables to be included in the HC while mitigating the impact of multicollinearity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The analyses were achieved through the FactoMineR package (v.2.11) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor the MFA, due to the variability of units, all the continuous variables were centred and normalised. For the ‘FPLS’, the missing quantitative data were replaced by their mean. Binary variables were excluded before conducting the MFA when one of the categories encompassed less than 3% of the records, as they would not inform clustering. All remaining variables and individuals were classified as either active or supplementary. Active variables and individuals are used to construct the high-dimensional Euclidean space, while supplementary variables are only projected afterwards onto it. They are, therefore, independent of the created Euclidean space. Supplementary individuals and variables were identified according to different criteria. First, outliers, defined as individuals (flocks) with very distinctive characteristics that contributed disproportionately to the first dimensions of the MFA compared to others, were set as supplementary individuals. Second, quantitative variables with no collinearity with other quantitative variables or calculated from other quantitative variables were kept as supplementary variables. Third, themes that could be used to interpret the dimensional space were defined as supplementary. All other variables were defined as active. Finally, the results of the MFA were described and more specifically, the variables’ contribution to the first two dimensions and their link to the supplementary qualitative variable based on an analysis of variance (F-test per variable and two-tailed Student’s t-test for the categories).\u003c/p\u003e\u003cp\u003eThe HC was then performed on the MFA projection. Clusters were identified from a dendrogram based on the Ward’s criterion [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The optimal number of clusters was inferred from the minimum ratio between the within-group inertias of two successive clusters. The clusters' clarity, coherence, and interpretability were the criteria used for their validation. Flocks creating a cluster of a single individual were set as a supplementary variable, and the analysis was rerun. To better describe the variability and similarities between the individuals, a two-tailed hypergeometric test for qualitative variables was used to identify the overrepresentation of variables in each cluster, and a two-tailed t-test was used to compare whether the mean of the quantitative variable of the cluster is equal to the overall mean [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive analysis\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eScreened flocks' production and health, and welfare indicators and contextual qualitative descriptors\u003c/h2\u003e\u003cp\u003eThe final dataset used for the analysis (i.e., the 115 \u0026lsquo;screened flocks\u0026rsquo;) contained data collected between January 2022 and April 2023. The median of the number of chicken houses per farm was two (minimum\u0026thinsp;=\u0026thinsp;1, maximum\u0026thinsp;=\u0026thinsp;21). The median number of flocks screened per farm was two (minimum\u0026thinsp;=\u0026thinsp;1, maximum\u0026thinsp;=\u0026thinsp;5). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides contextual information (i.e., farm type, region, season, ATB usage) about the screened flocks.\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\u003eDistribution of the contextual variables amongst the screened flocks (n\u0026thinsp;=\u0026thinsp;115).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContextual variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of flocks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% of flocks\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm type\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; 1\u0026ndash;2 chicken house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; \u0026gt;2 chicken houses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNUTS1\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; PL6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; PL8 South\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; PL8 North\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; PL9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; Autumn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; Spring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; Summer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; Winter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATB use\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24%\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\u003eAn overview of the production, health, and welfare indicators of the screened flocks is provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Information on foot-pad lesions was missing in 6% of the screened flocks (n\u0026thinsp;=\u0026thinsp;7). The analysis allowed the identification of one flock with extreme values: very high \u0026lsquo;mortality\u0026rsquo; of 40.69%, \u0026lsquo;FCR\u0026rsquo; of 3.29, an \u0026lsquo;EPEF\u0026rsquo; of 84.59 and a mean \u0026lsquo;age at slaughter\u0026rsquo; of 42 days. No additional information from the dataset could explain the mortality rate observed in this specific flock, implying that it is due to an event unrelated to the health issues explored in the available data. This flock was therefore considered an outlier for the rest of the analysis.\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\u003eDescription of the quantitative variables for the screened flocks and recorded flocks.\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance or health and welfare indicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScreened flocks (n\u0026thinsp;=\u0026thinsp;115)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEPEF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e388.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e395.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e487.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality (%)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40.690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDOA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCondemnation (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean weight at slaughter (Kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.990\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean age at slaughter (Day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e42.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.190\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPLS* (n\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecorded flocks (n\u0026thinsp;=\u0026thinsp;1,697)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEPEF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e403.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e405.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e771.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality (%)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-18.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40.690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDOA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCondemnation (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean weight at slaughter (Kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean age at slaughter (Day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e46.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.190\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPLS* (n\u0026thinsp;=\u0026thinsp;1,412)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e200.000\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\u003e\u003cem\u003eFor the tables\u0026rsquo; column name: SD as standard deviation, max as the maximum, min as the minimum. For the variable names: EPEF is the european production efficiency factor, DOA is the death on arrival, FCR is the feed conversion ratio, and FPLS is the foot-pad lesion score. *FPLS are the only variables with missing information; the number of the flocks with available data is given in parentheses. **Mortality is a calculated value based on the number of chickens ordered at the hatchery and the final number of chickens sold at the slaughterhouse, meaning that the mortality could be negative due to specific unrecorded events. For example, this could occur during the delivery of additional chicks from the hatchery or the introduction of additional chickens by the farmer during the production cycle.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe relationships between quantitative variables were further explored by examining correlations among them (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.A), meaning variables amongst the following themes: \u0026lsquo;Economic indicator\u0026rsquo;, \u0026lsquo;Production performance\u0026rsquo; and \u0026lsquo;Health and welfare\u0026rsquo;. \u0026lsquo;EPEF\u0026rsquo; was the single quantitative variable strongly correlated (higher than 0.5) with the others. Indeed, it is calculated from most other quantitative variables and was kept as a supplementary variable in the analysis. Amongst the remaining quantitative variables, the highest correlation was found between \u0026lsquo;DOA\u0026rsquo; and \u0026lsquo;condemnation\u0026rsquo; (|r| = 0.424; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All the other significant correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified between variables had a small correlation coefficient (|r| \u0026lt; 0.4 and |r| \u0026gt;0.2). These variables were defined as active variables for the rest of the analysis. On the other hand, \u0026lsquo;FPLS\u0026rsquo; (\u0026lsquo;Foot-pad lesion scores\u0026rsquo;) was the only variable without any significant correlation with any of the other variables and, as such, was defined as a supplementary variable for the next step of the analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eFigure A contains the screened flocks (n\u0026thinsp;=\u0026thinsp;115); in Figure B, recorded flocks were used for the analysis (n\u0026thinsp;=\u0026thinsp;1697).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe screened flocks showed significantly lower performance and health compared to the recorded flocks for the same \u0026lsquo;mean age at slaughter\u0026rsquo;: the \u0026lsquo;weight at the slaughter\u0026rsquo; of the screened flocks was lower (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while \u0026lsquo;mortality\u0026rsquo; and \u0026lsquo;DOA\u0026rsquo; were higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). Furthermore, the dataset\u0026rsquo;s correlation matrices changed depending on the group of flocks considered, suggesting that the screened flocks may not be representative of the entire flock population, the recorded flocks (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Indeed, the correlation among the quantitative variables was generally weaker in the recorded flocks compared to the screened flocks, with six comparisons losing significance (|r|\u0026lt; 0.2). However, a new correlation between age and weight at slaughter became significant (|r| = 0.245).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLaboratory qualitative data\u003c/h2\u003e\u003cp\u003eAn overview of the main screening results (\u0026lsquo;Necropsy lesions\u0026rsquo;, \u0026lsquo;Bacteria status\u0026rsquo;, \u0026lsquo;Eimeria status\u0026rsquo;, and \u0026lsquo;Viral circulation\u0026rsquo;) is provided in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u0026lsquo;Necrosis or ulcer of the musculoskeletal system\u0026rsquo; lesions, regrouping the common locomotor lesions \u0026lsquo;femoral head necrosis\u0026rsquo; and \u0026lsquo;feet-pad dermatitis\u0026rsquo;, were observed in almost all the screened flocks (n\u0026thinsp;=\u0026thinsp;106, 92.2%). Moreover, the majority of the screened flocks had at least one chicken presenting one of the high confidence score necropsy lesions: \u0026lsquo;uroliths in the ureters\u0026rsquo; (n\u0026thinsp;=\u0026thinsp;92, 80.0%), and \u0026lsquo;change in the composition (enlarged) of kidneys and/or ureters\u0026rsquo; (n\u0026thinsp;=\u0026thinsp;87, 75.7%). \u0026lsquo;Respiratory vascular congestion\u0026rsquo; (hyperaemia or ecchymosis) was the only lesion associated with a low confidence score observed in most flocks (n\u0026thinsp;=\u0026thinsp;89, 77.4%). Seventeen lesions were observed in less than 3% of the flocks. The corresponding variables were excluded from the analysis due to their scarcity.\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\u003eFrequency of necropsy lesions observed in the 115 screened flocks.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLesion type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConfidence score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of flocks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% of flocks\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMusculo-skeletal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNecrosis - ulcer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e92.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney and ureters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrates/ Uroliths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney and ureters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChange in composition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpleen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChange in composition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e41.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFibrin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFibrin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardio-vascular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFibrin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCelomic cavity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFibrin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChange in composition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMusculo-skeletal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVascular congestion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmpty or abnormal content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMusculo-skeletal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSwelling or oedema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVascular congestion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpleen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVascular congestion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVascular congestion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e41.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney and ureters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVascular congestion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardio-vascular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThinning, distended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSwelling or oedema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardio-vascular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChange in composition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardio-vascular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSwelling or oedema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.0\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\u003e\u003cem\u003eOnly lesions with a high or low confidence score are represented. Lesions associated with three or fewer flocks are also not presented, but available in the Additional file 3.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAmongst the thirteen bacteria taxa identified in at least 4 screened flocks (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), \u0026lsquo;\u003cem\u003eE. coli\u0026rsquo;\u003c/em\u003e was isolated in internal organs of one of the randomly selected flocks in almost two-thirds of the flocks (60.9%). The other bacteria taxa found in more than a fourth of the flocks were \u0026lsquo;\u003cem\u003eEnterococcus faecalis\u0026rsquo;\u003c/em\u003e (29.6%), \u0026lsquo;Staphylococcus spp.\u0026rsquo; (27.8%) and \u0026lsquo;\u003cem\u003eClostridium perfringens\u0026rsquo;\u003c/em\u003e (26.1%). In contrast, six rare bacteria taxa were identified in less than 3% of the flocks and were excluded from the dataset for the rest of the analysis.\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\u003eFrequency of bacteria taxa (species or genera) amongst the 115 screened flocks\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBacteria groups (species or genera) identified\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of flocks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% of flocks\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEscherichia coli*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEnterococcus faecalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus spp\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eClostridium perfringens*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEnterococcus cecorum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOrnithobacterium rhinotracheale\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEnterococcus faecium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBordetella avium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGallibacterium anatis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRiemerella anatipestifer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBordetella hinzii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEnterococcus hirae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.5\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\u003e\u003cem\u003eBacteria taxa associated with three or fewer flocks are not presented, but available in the Additional file 3. The asterisk highlights that the presence of E.coli and Clostridium perfringens was defined a bit differently than for the other bacteria due to its ubiquity, see the methodology for further details.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eA majority of the screened flocks (n\u0026thinsp;=\u0026thinsp;75, 65.2%) had no sign of Eimeria infestation. The cecum preferred by \u0026lsquo;\u003cem\u003eE. tenella\u0026rsquo;\u003c/em\u003e was the portion of the intestines most often colonised by oocysts (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e): 30.4% (n\u0026thinsp;=\u0026thinsp;35) of the flocks presented evidence of colonisation in at least one chicken. In contrast, the duodenum preferred by \u0026lsquo;\u003cem\u003eE. acervulina\u0026rsquo;\u003c/em\u003e was the section of the intestines that was the least colonised (n\u0026thinsp;=\u0026thinsp;11, 9,6%).\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\u003eFrequency of Eimeria infestation grade amongst the 115 screened flocks.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParasite infestation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of flocks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% of flocks\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuodenum\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeckel Diverticulum\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCecum\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRectum\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77%\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\u003eThe estimated circulation of IBDV, IBV and aMPV viruses is available in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Almost half (42%, n\u0026thinsp;=\u0026thinsp;48) of the sampled flocks showed evidence of field IBV virus circulation. Among those flocks, 70.8% were positive to the VAR 2 genotype (n\u0026thinsp;=\u0026thinsp;34), 22.9% to the 793B genotype (n\u0026thinsp;=\u0026thinsp;11),10.4% to the D274 genotype (n\u0026thinsp;=\u0026thinsp;5), 4.2% to the Mass genotype (n\u0026thinsp;=\u0026thinsp;2), 2.1% to the QX genotype (n\u0026thinsp;=\u0026thinsp;1) and IB80 genotype (n\u0026thinsp;=\u0026thinsp;1). All results are available in Additional file 3. The aMPV circulated in 20% of the flocks (n\u0026thinsp;=\u0026thinsp;23) and IBDV in 2% (n\u0026thinsp;=\u0026thinsp;2). However, virus genotypes for IBDV and aMPV were not explored further in the categories developed in Additional file 2.\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\u003eFrequency of virus circulation evidence amongst the 115 screened flocks.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVirus circulation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of flocks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% of flocks\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvian metapneumovirus (aMPV)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; SUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfectious bronchitis virus (IBV)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; SUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; HSUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfectious bursal disease virus (IBDV)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; SUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026hellip; Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003ch2\u003eMFA (Multifactor analysis)\u003c/h2\u003e\u003cp\u003eThe MFA was performed on all 115 screened flocks with the 53 variables available. All the variables associated with the aetiological and contextual themes were set as supplementary variables (i.e. contextual, economic indicators, bacteria status, Eimeria status, and virus circulation). Indeed, the information they contain can be used to characterise the observations related to production performance, health and welfare and necropsy lesions. Furthermore, two quantitative variables (i.e., \u0026lsquo;FPLS\u0026rsquo; and \u0026lsquo;EPEF\u0026rsquo;) and one flock (the identified outlier) were defined, respectively, as supplementary variables and as supplementary individuals. A first attempt at HC on this data set created a cluster containing a single flock. This flock had the highest condemnation rate (3.4%), the lowest mean weight at slaughter (1.70 kg) and the lowest mean age at slaughter (35 days), meaning that the production cycle was stopped early due to a specific event. As such, it was defined as an outlier and set as a supplementary individual. The final analysis was therefore done on 27 active variables and 113 active individuals.\u003c/p\u003e\u003cp\u003eThe first 21 dimensions obtained with the MFA represented 95% of the dataset's cumulative variability. The projection on the first two dimensions of the quantitative variables, the grouped variables and individuals are available in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, respectively. These two dimensions reflect 24.8% of the whole dataset.\u003c/p\u003e\u003cp\u003eThe correlation circle in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea illustrates how the main quantitative variables contribute to the first two dimensions, while Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb provides a synthetic comparison of the groups of variables. Furthermore, the variance analysis allows us to identify the qualitative categories, projected in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, that best characterise the coordinates of the individuals on the first two dimensions. Amongst the 27 variables related to necropsy lesions used in the analysis, eight of them best characterised the individuals on the first two dimensions: \u0026lsquo;respiratory fibrin\u0026rsquo;, \u0026lsquo;liver fibrin\u0026rsquo;, \u0026lsquo;cardiovascular fibrin\u0026rsquo;, \u0026lsquo;liver vascular congestion\u0026rsquo;, \u0026lsquo;celomic cavity fibrin\u0026rsquo;, \u0026lsquo;kidney and ureters change in composition\u0026rsquo;, \u0026lsquo;respiratory vascular congestion\u0026rsquo;, \u0026lsquo;gastrointestinal change in composition. The contextual and aetiological variables were not used to construct the MFA and, therefore, are fully independent of the first two dimensions. However, ten of the 26 contextual and aetiological variables were correlated with the first two dimensions. These ten variables included four bacteria taxa (i.e., \u0026lsquo;\u003cem\u003eStaphylococcus spp\u003c/em\u003e.\u0026rsquo;, \u0026lsquo;\u003cem\u003eOrnithobacterium rhinotracheale\u0026rsquo;, \u0026lsquo;E. coli\u0026rsquo;\u003c/em\u003e and \u003cem\u003e\u0026lsquo;Riemerella anatipestifer\u0026rsquo;\u003c/em\u003e), two viral circulation variables (i.e., confirmed circulation of IBDV and aMPV), one Eimeria status (i.e. \u0026lsquo;High cecum infestation\u0026rsquo;) and two contextual variables (i.e., the flock\u0026rsquo;s region \u0026lsquo;PL9\u0026rsquo; and \u0026lsquo;PL8a\u0026rsquo;).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe correlation circle of the quantitative variable by themes (a), groups (theme) representation (b) and projection of the qualitative categories (supplementary in blue, active in red, categories link to an absence, meaning, lesions or bacteria not found in the flock, in grey) significantly associated with either the first or the second dimension (c).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eHC (Hierarchical clustering)\u003c/p\u003e\u003cp\u003eA partition in three clusters was inferred from the minimum ratio between two successive within-group inertias and is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The clusters were named 1, 2 and 3 for ease, but a specific name was given based on the MFA active variables, which best characterised the group. All variables significantly characterising these groups are described in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e based on the characteristic of the theme they refer to (active, supplementary, qualitative or quantitative). The results, including the non-significant variables, are available in the Additional file 3.\u003c/p\u003e\u003cp\u003e\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\u003eMean and Standard deviation (SD) for each quantitative variable per cluster.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eCluster 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u003cp\u003eCluster 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c15\" namest=\"c12\"\u003e\u003cp\u003eCluster 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduction performance\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\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean age at slaughter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e39.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean weight at slaughter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth performance\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCondemnation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSupplementary\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEPEF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e392.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e387.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e415.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e27.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e331.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e35.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e-7.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPLS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e60.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e35.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e78.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e29.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e2.1\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\u003eThe variables came from quantitative themes set as active (\u0026lsquo;Production performance\u0026rsquo;, \u0026lsquo;Health performance\u0026rsquo;) or supplementary (\u0026lsquo;Economic indicator\u0026rsquo;) used in the MFA/HC and performed on 113 flocks for each cluster. For each cluster and quantitative variable, the results of the t-test comparing the cluster to the overall mean are gathered in the table with V for the value of the t-test and p for the p-value. Only significant variables are described, and p-values are coded using asterisks: * for p \u0026lt; 0.05, ** for p \u0026lt; 0.01, *** for p \u0026lt; 0.001.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrequency and proportion of each variable of the active qualitative theme \u0026lsquo;Context\u0026rsquo; used in the MFA/HC performed on 113 flocks and for each cluster.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eCluster 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u003cp\u003eCluster 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c16\" namest=\"c13\"\u003e\u003cp\u003eCluster 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContext\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePL6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePL8a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e28.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e20.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePL8b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e16.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePL9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e49.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e58.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLarge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e58.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e79.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e65.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e41.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e20.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAutomn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e31.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e41.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e29.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWinter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cp\u003eThe variables are from the qualitative theme \u0026lsquo;Context\u0026rsquo; used in the MFA/HC performed on 113 flocks. For each cluster and variable, the hypergeometric test results are gathered in the table with V for the sample estimate and p for the p-value. Only significant variables are described, and p-values are coded using asterisks: * for p \u0026lt; 0.05, ** for p \u0026lt; 0.01, *** for p \u0026lt; 0.001.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrequency and proportion of each variable from the active qualitative theme \u0026lsquo;Necropsy lesions per cluster.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eCluster 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u003cp\u003eCluster 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c16\" namest=\"c13\"\u003e\u003cp\u003eCluster 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLesion (High confidence score)\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular - Fibrin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e96.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e92.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCelomic cavity - Fibrin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e63.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e95.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e90.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney/ureters - Change in composition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e32.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e67.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e90.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eKidney/ureters \u0026ndash; Urates/ Uroliths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e36.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e79.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e63.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney/ureters - Vascular congestion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e65.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e45.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e34.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e54.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLiver - Fibrin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e98.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-3.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMusculoskeletal - Necrosis, ulcer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e22.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e92.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e96.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e77.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory - Fibrin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e98.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-3.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLesion (Low confidence score)\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular - Thinning distended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e85.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e68.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e54.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e45.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory -Vascular congestion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e63.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e81.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e90.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe variables, from the qualitative theme ‘Necropsy lesions’ used in the MFA/HC performed on 113 flocks, are regrouped according to their confidence scores. For each cluster and variable, the hypergeometric test results are gathered in the table with V for the sample estimate and p for the p-value. Only significant variables are described, and p-values are coded using asterisks: * for p \u003c 0.05, ** for p \u003c 0.01, *** for p \u003c 0.001. \u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrequency and proportion of each variable of the supplementary qualitative theme per cluster.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eCluster 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u003cp\u003eCluster 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c16\" namest=\"c13\"\u003e\u003cp\u003eCluster 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBacteria status\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBordetella avium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e82.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e95.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e88.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEscherichia coli**\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e48.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e40.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e39.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e51.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e59.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e60.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus spp\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e64.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e86.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e71.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e35.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e13.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e28.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEimeria status\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeckel's diverticulum - (\u003cem\u003eE. maxima\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e27.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e87.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e68.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCecum \u003cem\u003e(E. tenella\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e27.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e27.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e76.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e45.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-2.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRectum (\u003cem\u003eE. brunetti\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e13.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e22.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e81.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e63.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViral circulation\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaMPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e37.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e18.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-2.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e40.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e31.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe qualitative supplementary themes used in the MFA/HC performed on 113 flocks are \u0026lsquo;Bacteria status\u0026rsquo;, \u0026lsquo;Eimeria status\u0026rsquo;, and \u0026lsquo;Virus circulation\u0026rsquo;. For each cluster and variable, the hypergeometric test results are gathered in the table with V for the value of the test and p for the p-value. Only significant variables are described, and p-values are coded using asterisks: * for p \u0026lt; 0.05, ** for p \u0026lt; 0.01, *** for p \u0026lt; 0.001.\u0026nbsp;\u003c/p\u003e\u003cp\u003eHere add :\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e: \u003cem\u003eMean and Standard deviation (SD) for each quantitative variable per cluster.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e: \u003cem\u003eFrequency and proportion of each variable of the active qualitative theme \u0026lsquo;Context\u0026rsquo; used in the MFA/HC performed on 113 flocks and for each cluster.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e: \u003cem\u003eFrequency and proportion of each variable from the active qualitative theme \u0026lsquo;Necropsy lesions per cluster.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e: \u003cem\u003eFrequency and proportion of each variable of the supplementary qualitative theme per cluster.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eCluster 2 \u0026ndash; High-performing flocks\u003c/h2\u003e\u003cp\u003eCluster 2 included most of the flocks (n\u0026thinsp;=\u0026thinsp;64) and can be defined as a cluster of high-performing flocks associated with a low \u0026lsquo;FCR\u0026rsquo;, high \u0026lsquo;EPEF\u0026rsquo;, and high \u0026lsquo;weight at slaughter\u0026rsquo; (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These flocks also had better health performances, including fewer necropsy lesions. This was particularly true for fibrinous lesions in the liver, cardiovascular and respiratory tract. However, \u0026lsquo;necrosis or ulcers of the musculoskeletal system\u0026rsquo; were more common in this cluster than in the other. In terms of the presence of aetiological agent, Eimeria infestation, especially in the rectum, was less often observed, but two aetiological agents were over-represented, i.e., \u0026lsquo;\u003cem\u003eBordetella avium\u0026rsquo;\u003c/em\u003e and \u003cem\u003e\u0026lsquo;Staphylococcus spp\u0026rsquo;\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCluster 3 \u0026ndash; Low-performing flocks\u003c/h2\u003e\u003cp\u003eIn opposition to cluster 2, cluster 3 (n\u0026thinsp;=\u0026thinsp;22) can be defined as a cluster with flocks at an older age (longer production time) with low production and health performance raised in large farms (more than two chicken houses) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Indeed, these flocks had a higher \u0026lsquo;FCR\u0026rsquo;, lower \u0026lsquo;EPEF\u0026rsquo;, and a higher \u0026lsquo;age at slaughter\u0026rsquo;, higher \u0026lsquo;mortality rate\u0026rsquo; and higher \u0026lsquo;FPLS\u0026rsquo; than the others. Particularly, one necrotic lesion was more frequent: \u0026lsquo;vascular congestion in the kidneys or ureters\u0026rsquo;, which has a high confidence score. Among the aetiological agents, the cluster showed evidence of infestation by Eimeria. Indeed, high infestations in the cecum or evidence of infestations in the Meckel\u0026rsquo;s diverticulum were observed more frequently in the cluster\u0026rsquo;s flocks. Furthermore, there were significantly more evidence of circulation (confirmed or suspected) of aMPV in this cluster than in the others.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eCluster 1 \u0026ndash; Flocks with fibrinous lesions\u003c/h2\u003e\u003cp\u003eCluster 1 included 27 flocks and was characterised by birds slaughtered at a younger age and up to a lighter weight than the others. These flocks were generally raised on \u0026lsquo;small\u0026rsquo; farms (less than two chicken houses), in the region \u0026lsquo;PL9\u0026rsquo;. They were more frequently associated with fibrinous lesions in the liver, cardiovascular, respiratory, and celomic cavity. \u0026lsquo;Uroliths were also observed more frequently in this cluster than in others. All these lesions were associated with a high confidence score, meaning they are likely antemortem lesions. In opposition to the two other clusters, this cluster presented no specificities regarding economic performance (\u0026lsquo;EPEF\u0026rsquo;) but showed a higher condemnation rate. \u0026lsquo;\u003cem\u003eE. coli\u0026rsquo; was the o\u003c/em\u003enly aetiological agent significantly more present in this cluster.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we investigated the relevance of endemic contagious diseases in Polish broiler flocks by exploring patterns in production and health performance variables, necropsy lesions, and the presence of aetiological pathogens. Indeed, the presence of an aetiological pathogen is not sufficient to understand the influence of a contagious endemic disease on production performances, especially when more than one pathogen circulates. The study population consist of 115 flocks held in Poland between 2022 and 2023. They are part of the larger Polish broiler industry, which has greatly improved its efficiency in the past 30 years, and is the main European producer of poultry meat[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. More specifically, these flocks were produced just after the end of the COVID-19 pandemic during an avian flu epidemic, but in a period where low feed prices were prevalent, improving the Polish broiler production profitability [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this context, the production performance of the study flocks was above the European average published by Van Limbergen et al. (2020), but lower than the Polish study case farm results presented by Adaszyńska-Skwirzyńska et al. (2025). Notably, they had an above-average cumulative mortality rate compared to both studies (6.01\u0026thinsp;\u0026plusmn;\u0026thinsp;5.43 in the study flocks, compared to the European mean of 3.82% \u0026plusmn; 3.70). The study flocks were not associated with any major health issues to our knowledge (such as an avian flu outbreak), but were subject to high infectious pressure, as illustrated in our study by the diversity of pathogens found in these flocks: all three viruses from the screening assay (IBV, aMPV, IBDV) were detected in the study\u0026rsquo;s population (based on serology, vaccination history and PCR tests), as well as thirteen different bacteria and \u003cem\u003eEimeria spp\u003c/em\u003e. These aetiological agents could potentially explain the above-average mortality rates observed, especially as they were associated with a diversity of necropsy lesions in the birds (such as necrosis or ulcer of the musculoskeletal system\u0026rsquo;, \u0026lsquo;uroliths in the ureters\u0026rsquo; or \u0026lsquo;change in the composition (enlarged) of kidneys and/or ureters\u0026rsquo;). To go beyond this general overview, our study investigated whether patterns across the flocks in terms of production performance, health variables, and necropsy lesions could be observed in order to provide producers with a better understanding of their flocks' health.\u003c/p\u003e\u003cp\u003eUsing typologies to understand how the screened flock\u0026rsquo;s performance and diseases are associated\u003c/p\u003e\u003cp\u003eThree flock types were identified using the HC analysis, each defined by specific characteristics in terms of production performance and presence of different pathogens.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eHigh performing flocks\u003c/h2\u003e\u003cp\u003eThe high-performing flocks (cluster 2) show better health and production performances (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) compared with the overall recorded flocks (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), as well as with the European data published by Van Limbergen et al. (2020). Their production performances are close to Aviagen's 2022 performance objectives [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which are defined as the breed\u0026rsquo;s attainable standards, for a \u0026lsquo;mean age at slaughter\u0026rsquo; of 39 days (i.e., FCR of 1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 and 1.47 and average \u0026lsquo;weight at slaughter\u0026rsquo; of 2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 kg and 2.7 kg for the high-performing flocks and Aviagen standards, respectively). These results highlight the strong production performance capacities of the studied farms. However, even if the cluster\u0026rsquo;s mean mortality (4.5% \u0026plusmn; 2.4) was the lowest identified in our study, it remains higher than the reported European mean of 3.82% \u0026plusmn; 3.70 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], illustrating the potential for improvements in production performance. The reason for the relatively high mortality in these high-performing flocks is unclear, but it could indicate that the infectious pressure observed among all screened flocks also impacts, in part, those flocks, despite their good production performances. However, issues arising from rapid growth or other environmental factors cannot be ruled out.\u003c/p\u003e\u003cp\u003eTwo other characteristics defined those high-performing flocks. First, 96.9% of them presented \u0026lsquo;Musculoskeletal necrosis or ulcer\u0026rsquo; lesions, the majority of which were necrosis of the femoral head (result not presented). These lesions are usually associated with higher body weight and increased stocking density of birds [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The \u0026lsquo;mean body weight at slaughter\u0026rsquo; of the birds in the cluster is indeed slightly but significantly higher than the screened flocks (i.e., 2.6 kg and 2.5 kg, respectively). However, no information on their stocking density was available. These lesions are a common indicator of welfare issues in broiler intensive production. Furthermore, if these lesions develop spontaneously, \u0026lsquo;\u003cem\u003eStaphylococcus spp.\u0026rsquo;\u003c/em\u003e is one of the main bacterial taxa associated with these lesions during secondary infection processes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These bacteria were also overrepresented in this cluster. Exploring the practices in place in the Polish production context further could potentially help define sustainable and adaptable solutions for producers to mitigate this welfare issue without compromising current productivity levels. Second, \u0026lsquo;\u003cem\u003eBordetella avium\u0026rsquo;\u003c/em\u003e was more frequently present in these flocks than in the others. This bacterium causes Turkey coryza, but its pathogenicity is considered opportunistic in broilers [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Its presence is not known to impact production performance. Hence, the presence of \u003cem\u003eBordetella avium\u003c/em\u003e in 11 of the high-performing flocks does not suggest any pathogenicity as well.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eLow performing flocks\u003c/h2\u003e\u003cp\u003eThe low-performing flocks (cluster 3) exhibit lower production and health performance, which can be put into perspective with the presence of two aetiological agents commonly observed in these flocks. The first one, \u003cem\u003eEimeria spp\u003c/em\u003e., has been estimated to reduce a production performance indicator, to increase \u0026lsquo;FCR\u0026rsquo; and increase \u0026lsquo;FPLS\u0026rsquo; [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] as observed in cluster 3. However, estimating the impact of coccidiosis on production performance remains complex, especially in subclinical cases, as it is multifactorial, and co-infection with bacteria such as \u003cem\u003eClostridium Perfringens\u003c/em\u003e creates significant variation in production performance [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. If causality between low production performance and coccidiosis cannot be assumed in this study, \u003cem\u003eEimeria spp\u003c/em\u003e. remains a major issue in broiler production, especially as pressure to reduce the use of coccidiostats (antimicrobial compounds) is growing [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Unfortunately, the lack of information regarding coccidiosis management practices does not allow this study to make any further conclusions.\u003c/p\u003e\u003cp\u003eThe other more frequent aetiologic agent present in cluster 3 was aMPV. This observation was made taking into consideration serology and vaccine history, meaning that identification of vaccine strains is not impossible but limited. Furthermore, in the past ten years, the burden of this respiratory pathogen on European broiler flocks has been rising [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], while diverse aMPV subtype B strains are being reported across Europe, with significant diversity compared to the initial strains introduced in France in 1985 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Recent observations in Poland (Śmiałek 2024, unpublished) suggest that aMPV infections have more significant health impacts at the end of the production, surpassing the impact observed with either IBV and IBDV. Our study shows an association between poor production performances and the presence of aMPV. Therefore, it provides an additional incentive to investigate further aMPV and its potential impact on broilers at the end of their production cycle. In the meantime, vaccination remains the main aMPV control measure [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eFlocks with fibrinous lesions\u003c/h2\u003e\u003cp\u003eThe last type of flocks (cluster 1) shows high frequencies of fibrinous lesions at necropsy and a higher condemnation rate, suggesting a probable sub-acute/chronic contagious disease affecting the flocks at the end of production [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Despite the observed evidence of infection, these flocks did not perform worse than the average flock, especially on the economic \u0026lsquo;EPEF\u0026rsquo; indicator. However, they were generally slaughtered at a smaller weight and a younger age. This could be due to the common practice of sending chickens to the slaughterhouse earlier than planned when clinical symptoms start to appear, to minimise potential losses. Furthermore, fewer treatments, especially antibiotics, are available for use due to the withdrawal period for human consumption, pushing producers to slaughter earlier to maximise their benefits instead of attempting treatment.\u003c/p\u003e\u003cp\u003eThe only aetiological agent associated with this cluster was avian pathogenic \u003cem\u003eE. coli.\u003c/em\u003e The necropsy results in the cluster\u0026rsquo;s flocks are also consistent with literature reports about colibacillosis, even if the lesions are not specific: perihepatitis, airsacculitis, pericarditis and peritonitis [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Colibacillosis is also known as a major cause of carcass condemnation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], which is consistent with the observed high rate of condemnation in the cluster. Furthermore, the infection can be primary, but is more often considered secondary [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Our study did not identify other aetiological agents associated with the high prevalence of \u003cem\u003eE. coli.\u003c/em\u003e Indeed, as sampling only occurred once at the end of production, this might have been too late for the primary causes of E. coli to be identified. Further work would be needed to study the potential primary source of infection or predisposing factors. To initiate the investigation, characteristics of the cluster can be utilised. For example, the flocks in this cluster were most frequently associated with the region (PL9), which is characterised by a higher density of poultry production (Smialek 2025, unpublished data). The burden of a diverse set of endemic contagious diseases in highly dense areas has been shown to play a role [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Their single or grouped contribution to the performances of these flocks should be further investigated, especially as our study did not identify a specific aetiological agent more frequently present in these flocks. Moreover, environmental issues such as inadequate ventilation and poor hygiene practices have been identified as predisposing factors and should not be dismissed [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond the clusters\u003c/p\u003e\u003cp\u003eFour additional aetiological agents were associated with the first two dimensions of the MFA reflection, 24.8% of the dataset\u0026rsquo;s variability, and can provide further insight into the health and performance of our study population.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eOther aetiologic agents associated with specific performance and necropsy profiles\u003c/h2\u003e\u003cp\u003eAmong the aetiological agents identified as significant in the MFA, one virus (IBDV) and two bacteria taxa (\u0026lsquo;\u003cem\u003eOrnithobacterium rhinotracheale\u0026rsquo; and \u0026lsquo;Riemerella anatipestifer\u0026rsquo;\u003c/em\u003e) do not appear in the clustering analysis, which can be used to draw further conclusions from our study.\u003c/p\u003e\u003cp\u003eAll of those aetiological agents were pathogens of poultry flocks (i.e., \u0026lsquo;IBDV\u0026rsquo;, \u0026lsquo;\u003cem\u003eOrnithobacterium rhinotracheale\u0026rsquo;\u003c/em\u003e, and \u0026lsquo;\u003cem\u003eRiemerella anatipestifer\u0026rsquo;\u003c/em\u003e) associated with low production and health performances (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). For example, the only two flocks infected with IBDV despite vaccination exhibited low production and health performance, which is consistent with the immunosuppressive effects of IBDV infections [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This observation remains limited to two flocks, and to draw any conclusions, it would be necessary to know if more similar observations were made in other flocks. If the answer is yes, then it raises questions about the current vaccination protocol: are they properly carried out or could the vaccination failure be due to the presence of new IBDV strains circulating in Central Europe [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Concerning flocks with \u0026lsquo;\u003cem\u003eOrnithobacterium rhinotracheale\u0026rsquo;\u003c/em\u003e, they were characterised by the presence of fibrinous lesions and poor health performance (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), which is expected for a respiratory pathogen observed in secondary infections in broilers [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Similarly, \u003cem\u003eRiemerella anatipestifer\u003c/em\u003e is a respiratory pathogen that is scarcely described in broilers in the literature, but is more commonly described in duck or turkey flocks [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In our study, we observed few cases of flocks infested with \u0026lsquo;\u003cem\u003eRiemerella anatipestifer\u0026rsquo;\u003c/em\u003e (7%, 8 flocks). Furthermore, in the MFA, the bacterial taxa had close coordinates to \u0026lsquo;\u003cem\u003eOrnithobacterium rhinotracheale\u0026rsquo;\u003c/em\u003e. Indeed, infection caused by both bacteria taxa in broilers can be misdiagnosed before laboratory testing as one another or as other aetiologic agents. \u003cem\u003eRiemerella anatipestifer\u003c/em\u003e\u0026rsquo;s presence in flocks supports the need to investigate further the impact of this disease on Polish broiler production and raises questions about the route of infection, illustrating the importance of biosecurity and good hygiene practices in dense multi-species poultry production regions [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003ePrevalent pathogen and lesions not identified in the analysis\u003c/h2\u003e\u003cp\u003eIBV was the virus with the most flocks with evidence of circulation (after treatment of vaccine history, serology and RT-PCR results) in our study population (42% of the study flock), but was not associated with any flock types or MFA dimensions. This is surprising because its health burden is reported to be one of the heaviest on the poultry industry, including in Poland [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Hence, this observation could be due to the isolation of vaccine strains; however, the use of serology and vaccine information limits this hypothesis. On the other hand, Legnardi et al. (2019) investigated IBV circulation in Poland and reported the absence of systemic clinical signs in flocks despite evidence of virus circulation. They concluded that the widespread vaccination in Poland is effective in reducing the current IBV burden. Similar observations from Italy and France were reported [\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This could explain why we did not observe a relation between the presence of IBV and our production and health variables, indicating that despite its wide presence in the population, the virus is not affecting health and production performance. Legnardi et al. (2019) also described a specificity concerning the Polish poultry industry, where a large number of IBV vaccination programs were being followed without any clear rationale. During this study, we observed similar practices: 25 different vaccination programs were used across the 115 flocks from 59 different farms (results not shown). This practice increases the risk of vaccine virulence reversion and could play a role in the future emergence of new IBV strains [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Beyond the impact of IBV on flocks, our study re-highlights the need for timely contextual information to move towards a more rational approach towards IBV vaccination management.\u003c/p\u003e\u003cp\u003eIn addition to aetiological agents, two major necrosis lesions in the urinary tract were identified at unexpected frequency: the presence of uroliths (80%) and \u0026lsquo;change in the composition (enlarged) of kidneys and/or ureters\u0026rsquo; (76%). Neither lesion was associated with any flock type, providing little additional information. In this context, further research is needed to understand their cause/origin better.\u003c/p\u003e\u003cp\u003eChallenges and limitations of data re-use\u003c/p\u003e\u003cp\u003eIn this study, routinely collected data from farmers and veterinary laboratories were reused for research purposes, providing insight into the health and production performance of broiler flocks in Poland. However, reusing this available data for research is associated with some limitations. First, the flocks included in the analysis were not randomly selected. Our results confirmed that the production and health performance of screened flocks were not representative of the entire study population. Producers may have preferentially selected flocks with poorer performances for screening. Therefore, any generalisation of results should be done with care, taking into consideration these selection biases. Second, data on farm management, nutrition and environmental factors were not available, which limited the extent of our analysis, as illustrated, for example, by the absence of information on coccidiosis management practices, stocking density, or production environmental status (such as ventilation and temperature).\u003c/p\u003e\u003cp\u003eFurthermore, it was the first time that these farms and the laboratory had made their data available to an external researcher for digital reuse at this scale. To do so, in the absence of a comprehensive automated data management process, they manually manipulated the data by integrating multiple spreadsheets or transcribing PDFs into spreadsheets. Such processes are known to create errors in the data. To mitigate this issue for the transcribed data, any discrepancies identified were sent to the laboratory for correction and validation. The next steps of data integration and preparation for analysis were also automated to minimise further errors. Fully estimating the impact of such data errors is not possible.\u003c/p\u003e\u003cp\u003eAdditionally, the fact that these data were routinely collected for production purposes is associated with some limitations. First, the laboratory techniques used were defined to provide an operational screening for the farmer and their veterinarian, specific to their context, and not to be compared with other research results that answer specific research questions. For example, in studies examining the presence of IBV, techniques based on the complete sequencing of the S1 gene (the region where most of the IBV genetic variability is concentrated) are used, but these techniques are more expensive, more time-consuming and harder to implement [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Currently, they cannot be used for routine field screening by the Polish veterinary laboratory. Similarly, production performance data were created primarily to fulfil the industry\u0026rsquo;s needs. For example, \u0026lsquo;mortality\u0026rsquo; is calculated by farms as the number of chickens sold minus the number of chicks and is mainly used to calculate the flock's economic benefit. A few \u0026lsquo;recorded\u0026rsquo; flocks showed a negative \u0026lsquo;mortality\u0026rsquo; (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), illustrating the fact that this value is an estimation and not the true \u0026lsquo;cumulative mortality\u0026rsquo;. Indeed, it assumes that the no chicken movement occurred, which is indeed rare, and that the number of chicks ordered at the hatchery is the number delivered, which is often not true. Indeed, hatcheries can send off additional chicks to the order to anticipate any loss due to transport or for other commercial reasons, meaning that mortality in our study can be underestimated. However, considering that the mortality observed was generally high, the impact of this bias on our observation is expected to be limited.\u003c/p\u003e\u003cp\u003eUntapped potential of data re-use\u003c/p\u003e\u003cp\u003eReusing routinely collected data on animal health to enhance contextual knowledge of endemic contagious diseases has been promoted to increase coverage and timely data collection in ways that traditional data collection through research surveys cannot [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. However, we found only a few examples of similar work that integrated at least two different data sources and did not rely on an additional farm survey to fulfil the research goal [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. As described above, our study lacked the coverage typically associated with reusing routinely collected data and was subject to the biases common to data reuse. Despite these limitations, due to the diversity of the collected variables, the study was able to identify and formulate multiple hypotheses to support future design studies, which could later be used to provide recommendations to producers. The study also serves as a practical example of the value of data reuse for health management, providing a multivariate description of flock health and demonstrating the importance of investing in improved data management and collection systems for the industry. This is even more essential when investment in integrated health data systems is known to be costly, especially for small-scale farms, such as many of them in Poland [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBy illustrating the value of data routinely collected by the industry for generating new knowledge, our study therefore advocates for better and wider access to such data by creating systems and data flows that integrate relevant information to generate animal health intelligence [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study enabled the identification of three groups of flocks defined by specific performance status and necropsy lesions associated with known pathogens relevant to Polish broiler production, by reusing and integrating data regularly produced by the industry. As such, it demonstrates the value of this data in enhancing the monitoring and understanding of endemic contagious diseases and their interactions within a specific context. This study provides an example of how such data can be used to provide farmers and veterinarians with a deeper understanding of the primary characteristics and disease issues affecting their flocks, offering contextual information and hypotheses to enhance their disease prevention and management efforts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor contribution\u003c/p\u003e\n\u003cp\u003eC.D. cleaned and integrated the data shared by data owners with the support of M.S., who communicated with the data owners. C.D. and C.F. designed and performed the study. C.D. carried out the analysis with input from C.F. for statistical analysis and from M.S. and J.J.W. for interpretation of laboratory results. C.D. and C.F. wrote the manuscript with feedback and support from M.S. and J.J.W. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate:\u003c/p\u003e\n\u003cp\u003eNot applicable. The study reuses data that were originally produced for another purpose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests:\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgement:\u003c/p\u003e\n\u003cp\u003eWe want to thank SLW Biolab and the broiler producers for sharing the data for this study. Specifically, we would like to extend special thanks to Marta Gańko, Ilona Czokajło,\u0026nbsp;and Karolina Kowalewska, who collected the data across all the laboratory systems and answered our questions about it. We would also like to thank the Royal GD pathologists who shared their confidence level in the different necropsy lesions available in the data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work has received funding from the European Union\u0026rsquo;s Horizon 2020 research and innovation program under grant agreement No. 101000494 (DECIDE).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData Availability:\u003c/p\u003e\n\u003cp\u003eThe data analysed during the current study are available in the Zenodo repository, DOI: 10.5281/zenodo.17406208.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCode availability:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe code supporting this article\u0026apos;s conclusion is available in a Zenodo repository linked to a public GitHub repository, DOI: 10.5281/zenodo.17424234. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFAO. 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Prev Vet Med. 2021;187:105235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.prevetmed.2020.105235\u003c/span\u003e\u003cspan address=\"10.1016/j.prevetmed.2020.105235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e REGULATION (EU) 2017/625 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 15 March 2017.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Endemic disease, Poultry, Data reuse, Cluster Analysis, Health, Production performance","lastPublishedDoi":"10.21203/rs.3.rs-7939430/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7939430/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eEndemic contagious diseases in broilers have a significant impact on production performance. However, endemic contagious diseases are multifaceted and complex. They are rarely monitored on a large scale. This complexity hinders their mitigation, as timely information about their distribution and knowledge about their impact on production performance is scarce.\u003c/p\u003e\n\u003cp\u003eThis study aimed to evaluate whether data routinely produced by the Polish broiler industry, the first European meat producer, could be reused to generate knowledge about those diseases and provide stakeholders with contextual information to improve their disease management.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eThe study reused a dataset collected by a large producer and a veterinary laboratory, which implemented a screening program at the end of the production cycle. The high-dimensional dataset covered 115 flocks produced between 2018 and 2023 across Poland. It contained information on production indicators, health indicators, necropsy lesions, and a list of evidence of infection or infestation by a diverse range of aetiological agents (bacterial, viral, and \u003cem\u003eEimeria\u003c/em\u003e). The screened flocks, despite strong production performance indicators, presented a higher mortality rate and a large diversity of pathogens. The cluster analysis enabled the identification of three flock profiles, connecting the observation variables (health, production indicator, necropsy lesions) to the aetiological agents. Flocks from the first cluster were described as a flock with high rates of fibrinous lesions, with a high condemnation rate associated with the identification of \u003cem\u003eE. coli. \u003c/em\u003eThe second cluster was defined by high production performances but also higher rates of femoral head necrosis. The flocks from the last cluster had lower production performance, showing evidence of strong infestation by Eimeria spp. and evidence of avian metapneumovirus circulation.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eThe study is an example of how high-dimensional data produced by the broiler industry can be reused and integrated to create contextual knowledge for farmers and veterinarians about endemic contagious diseases. Access to this timely contextual knowledge is essential to enhance disease prevention and management efforts for farmers, veterinarians, and broiler industry stakeholders.\u003c/p\u003e","manuscriptTitle":"Integrating heterogeneous data to address endemic diseases in broiler production: insights from a Polish case study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 07:14:31","doi":"10.21203/rs.3.rs-7939430/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-15T14:05:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T18:36:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335443966139500103189421823971635768124","date":"2025-11-12T17:28:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T09:38:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268571359174858405938523692760608137543","date":"2025-11-05T12:23:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T20:36:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-29T05:05:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-28T02:47:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-28T02:46:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2025-10-24T10:03:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90cad808-9b1a-4ec5-ac42-f03325cbd0ab","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:03:28+00:00","versionOfRecord":{"articleIdentity":"rs-7939430","link":"https://doi.org/10.1186/s12917-026-05341-x","journal":{"identity":"bmc-veterinary-research","isVorOnly":false,"title":"BMC Veterinary Research"},"publishedOn":"2026-03-09 15:59:41","publishedOnDateReadable":"March 9th, 2026"},"versionCreatedAt":"2025-11-14 07:14:31","video":"","vorDoi":"10.1186/s12917-026-05341-x","vorDoiUrl":"https://doi.org/10.1186/s12917-026-05341-x","workflowStages":[]},"version":"v1","identity":"rs-7939430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7939430","identity":"rs-7939430","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}