Prevalence and geographic distribution of Eimeria species on commercial broiler farms in Guangdong, China

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This study aims to conduct an epidemiological investigation into the occurrence of Eimeria species and associated risk factors under intensive management conditions across four regions in Guangdong province, China. Results A total of 394 fecal samples were obtained from 89 broiler chicken farms, culminating in an overall positivity rate of 87.06%. The results showed that the identification of all seven Eimeria species, with E. acervulina (36.29%), E. mitis (35.03%), E. tenella (34.52%) and E. necatrix (30.96%) emerging as the most prevalent species. Remarkably, single-species infections were observed in 42.86% of instances, while two to three species mixed infections were detected in 39.94% of the samples. Moreover, brid age, farming practices, control strategies, farm locations, and the presence of necrotic enteritis (NE) proved significant risk factors. Notably, a strong correlation was observed between brid age, particularly in adult birds, and the occurrence of E. necatrix ( p < 0.001). A significant correlation was identified between the infection of E. necatrix or E. acervulina and the presence of NE in flocks ( p < 0.001). Flocks from northern Guangdong and Peal River delta displayed higher prevalence of E. necatrix ( p < 0.05). Flocks under the control programs incorporating live vaccines correlated strongly with E. tenella – E. brunetti infections ( p < 0.05). Conclusions Molecular analysis undertaken in this study, coupled with the correlation results, furnishes compelling evidence. Nevertheless, it is imperative to underscore the necessity for further surveys to delve deeper into the occurrence of different Eimeria species under intensive management conditions, which will contribute significantly to our knowledge of coccidia control in poultry. Broiler Eimeria spp. Prevalence Risk factors Figures Figure 1 Figure 2 Background Coccidiosis ranks among the foremost and widespread disease affecting chickens worldwide. Triggering by protozoan parasites belonging to the Eimeria genus, coccidiosis inflicts severe damage to the intestinal tract, resulting in highened mortality rates, reduced weight gain, impaired nutrient absorption, and increased susceptibility to other enteric pathogens [ 1 ]. The far-reaching repercussions of this disease translate into a profound economic impact on poultry industry [ 2 ]. In chickens, seven species, namely, E. tenella , E. necatrix , E. brunetti , E. acervulina , E. maxima , E. mitis , and E. praecox have been recognized, each with a proclivity for specific segments of the intestinal tract and exhibiting different pathogenicity, yielding distinct clinical manifestations [ 3 ]. E. necatrix , for instance, emerges as the most pathogenic species, alongside the relatively prevalent E. tenella , both induce bloody lesions and give rise to elevated morbidity and mortality rates in chickens [ 4 ]; E. brunetti exhibits highly pathogenic and is associated with haemorrhagic coccidiosis [ 5 ]; Conversely, E. acervulina and E. maxima are classifies as moderately pathogenic, provoking inflammation of the intestinal wall characterized by pinpoint haemorrhage and epithelial demolition [ 5 ]. Finally, E. mitis , and E. praecox are generally considered less pathogenic, causing malabsorption and enteritis [ 3 ]. Control strategies mainly based upon chemotherapy or vaccination. However, the development of drug resistance over time in different parts of the world and the lack of new anticoccidial drugs have reduced efficacy of anticoccidial agents [ 6 ]. Anticoccidial live vaccines have been used to prevent coccidiosis since last decades [ 7 ]. There are three types of live anticoccidial vaccines currently available for use in China, including trivalent vaccine containing E. tenella , E. acervulina and E. maxima ; tetravalent vaccine containing E. tenella , E. necatrix , E. acervulina and E. maxima ); and one importing vaccine Coccivac™ containing E. maxima , E. mivati , E. acervulina and E. tenella . To estimate the efficiency of the current control strategies, including the composition of vaccines, it is essential to understand the epidemiology of Eimeria species, as well as potential risk factors associated with occurrence of different Eimeria species. The conventional taxonomy of Eimeria species has relied on morphological attributes, the segments of the intestinal tract affected and the pre-patent period of the Eimeria following in vivo infection in chickens [ 5 ]. Nevertheless, these conventional methods may fall short of achieving precise diagnosis [ 8 ]. In recent times, polymerase chain reaction (PCR) techniques have emerged as a valuable tool for the identification of all seven Eimeria species. This molecular method employs genetic markers situated within the internal transcribed spacer-1 (ITS-1), ITS-2, and the sequence characterized amplified region (SCAR) [ 9 – 12 ]. As of now, there is a conspicuous absence of accurate data or previously reported information concerning the prevalence of Eimeria species in broiler chicken farms in Guangdong province, China. Therefore, the purpose of this study is to investigate the epidemiology of Eimeria species in Guangdong province and simultaneously analyze the risk factors associated with the presence of different Eimeria species. The finding from this study will not only contribute to our understanding of the occurrence and potential control strategies for coccidiosis in poultry in Guangdong province, China, but also enhance our comprehension of the potential risk factors associated with intensive poultry management practices. Results Eimeria species occurrence in different regions in Guangdong In this study, we observed a 87.06% (343/394) prevalence of Eimeria species in Guangdong. All Eimeria species were detected in four regions of Guangdong (Table 1 ), with E. acervulina (36.29%), E. mitis (35.03%), E. tenella (34.52%) and E. necatrix (30.96%) emerged as the predominant species. Geographically, E. necatrix had a wider distribution in northern Guangdong and Pearl River delta (45.97% and 31.86%, p < 0.001), while E. acervulina was more prevalent in eastern and western Guangdong (47.13% and 44.29%, p < 0.05). Single-species infections were common across all four regions of Guangdong (42.86%). Additionally, 39.94% of samples contained 2 to 3 Eimeria species in a single fecal sample (Fig. 1 ). Overall, the most prevalent combinations across the four regions were E. acervulina – E. tenella (15.48%), followed by E. acervulina – E. necatrix (14.21%), E. acervulina – E. tenella – E. mitis (6.85%, p < 0.05), and E. acervulina – E. tenella – E. necatrix (6.60%). Specifically, the prevalence of both E. acervulina – E. tenella (22.99%) and E. acervulina – E. tenella- E. mitis (13.79%) was highest in eastern regions, while both E. acervulina – E. necatrix (19.47%) and E. acervulina – E. tenella- E. necatrix (8.85%) were most prevalent in Pearl River delta (Table 2 ). Table 1 Prevalence of Eimeria infection in broiler chickens by studied regions of Guangdong province over 2020–2021 (n – total number of samples; 95% CI: 95% confidence interval; significant predictors in bold) Eimeria species Pathogenicity group All (n = 394) Eastern (n = 87) Western (n = 70) Northern (n = 124) Pearl River delta (n = 113) P -value Positive (95% CI) Positive (95% CI) Positive (95% CI) Positive (95% CI) Positive (95% CI) Any Eimeria species — 87.06 (83.73–90.38) 93.10 (87.67–98.54) 90.0 (82.80–97.20) 86.29 (80.15–92.43) 81.42 (74.13–88.70) 0.085 Eimeria necatrix Very high 30.96 (26.38–35.55) 13.79 (6.40-21.19) 24.29 (13.99–34.58) 45.97 (37.07–54.86) 31.86 (23.14–40.58) < 0.001 Eimeria tenella High 34.52 (29.80-39.23) 45.98 (35.29–56.66) 25.71 (15.22–36.21) 31.45 (23.16–39.74) 34.51 (25.61–43.41) 0.047 Eimeria brunetti High 19.54 (15.61–23.48) 21.84 (12.98–30.70) 17.14 (8.09–26.19) 16.94 (10.24–23.63) 22.12 (14.35–29.90) 0.668 Eimeria acervulina Medium 36.29 (31.53–41.06) 47.13 (36.43–57.83) 44.29 (32.36–56.22) 25.0 (17.27–32.73) 35.40 (26.45–44.35) 0.004 Eimeria maxima Medium 18.27 (14.44–22.11) 27.59 (18.01–37.17) 15.71 (6.97–24.45) 17.74 (10.92–24.56) 13.27 (6.92–19.63) 0.064 Eimeria mitis Low 35.03 (30.29–39.76) 34.48 (24.29–44.67) 31.43 (20.28–42.58) 37.10 (28.48–45.72) 35.40 (26.45–44.35) 0.885 Eimeria praecox Low 16.50 (12.82–20.18) 24.14 (14.96–33.31) 10.0 (2.80–17.20) 12.90 (6.92–18.89) 18.58 (11.30-25.87) 0.061 Table 2 Prevalence of mixed infections in broiler chickens from Guangdong province (n – total number of samples; 95% CI: 95% confidence interval; significant predictors in bold) Eimeria species a All (n = 586) Eastern (n = 87) Western (n = 70) Northern (n = 87) Pearl River delta (n = 150) P -value Positive (95% CI) Positive (95% CI) Positive (95% CI) Positive (95% CI) Positive (95% CI) EA–ET 15.48 (11.89–19.07) 22.99 (13.97–32.01) 15.71 (6.97–24.45) 9.68 (4.40-14.95) 15.93 (9.08–22.78) 0.073 EA–EN 14.21 (10.75–17.68) 9.20 (3.0-15.39) 7.14 (0.96–13.33) 16.94 (10.24–23.63) 19.47 (12.66–26.88) 0.046 EA–ET–EN 6.60 (4.14–9.06) 6.90 (1.46–12.33) 1.43 (0-4.28) 7.26 (2.63–11.89) 8.85 (3.53–14.17) 0.225 EA–ET–EMI 6.85 (4.34–9.36) 13.79 (6.40-21.19) 1.43 (0-4.28) 4.84 (1.01–8.67) 7.08 (2.28–11.88) 0.014 a Eimeria species: E. acervulina (EA), E. tenella (ET), E. necatrix (EN), E. mitis (EMI). Risk factors associated with Eimeria species occurrence Univariate analysis revealed significant associations between the prevalence of Eimeria species and several variables, including bird age, management type, drinking water source, control strategy, presence of NE, and location of farm (Table 3 ). Bird breed and flock size, however, did not considered to the multivariate analysis. Logistic regression identified several key risk factors associated with E. necatrix infection, with adult birds (OR = 10.90; 95% CI: 4.91–24.19; p < 0.001), NE (OR = 3.22; 95% CI: 1.93–5.37; p < 0.001), flocks from northern Guangdong (OR = 4.14; 95% CI: 1.12–15.32; p < 0.05) and Peal River delta (OR = 3.29; 95% CI: 1.01–10.79; p < 0.05) demonstrated a notably higher risk. Likewise, flocks vaccinated with anticoccidial live vaccines (OR = 4.52; 95% CI: 1.21–16.91; p < 0.05) and presence of NE (OR = 1.82; 95% CI: 1.19–2.78; p < 0.05) were positively associated with a higher likelihood of E. tenella – E. brunetti infection. Flocks with the presence of NE (OR = 2.69; 95% CI: 1.76–4.11; p < 0.001) were also associated with higher prevalence of E. acervulina – E. maxima infection. Furthermore, our results indicated that flocks with grower birds (OR = 2.90; 95% CI: 1.60–5.26; p < 0.001), adult chickens (OR = 1.99; 95% CI: 1.11–3.56; p < 0.05), along with the occurrence of NE (OR = 1.57; 95% CI: 1.04–2.38; p 0.05) (Table 4 ). Table 3 Univariable logistic regression analysis of risk factors associated with prevalence of Eimeria in chickens in Guangdong province (n – total number of samples; 95% CI: 95% confidence interval; OR: odds ratios; significant predictors in bold) Variables Category (n) All Very high pathogenicity ( E. necatrix ) High pathogenicity ( E. tenella + E. brunetti ) Medium pathogenicity ( E. acervulina + E. maxima ) Low pathogenicity ( E.mitis + E. praecox ) Positive (95% CI) Positive (95% CI) OR (95% CI) P -value Positive (95% CI) OR (95% CI) P -value Positive (95% CI) OR (95% CI) P -value Positive (95% CI) OR (95% CI) P -value Age Starter (n = 96) 86.45 (79.49–93.43) 12.50 (5.76–19.24) Referent — 37.50 (27.64–47.36) Referent — 46.88 (36.71–57.04) Referent — 29.17 (19.91–38.42) Referent — Grower (n = 150) 84.67 (78.83–90.50) 23.33 (16.49–30.18) 2.13 (1.04–4.35) 0.038 44.67 (36.62–52.71) 1.35 (0.80–2.27) 0.267 41.33 (33.36–49.30) 0.80 (0.48–1.34) 0.393 53.33 (45.26–61.41) 2.78 (1.61–4.79) < 0.001 Adult (n = 148) 89.86 (84.95–94.78) 50.68 (42.53–58.82) 7.19 (3.62–14.27) < 0.001 45.27 (37.16–53.38) 1.38 (0.82–2.33) 0.231 43.92 (35.83–52.01) 0.89 (0.53–1.49) 0.650 42.57 (34.51–50.63) 1.80 (1.04–3.11) 0.035 Breed Indigenous CBS (n = 190) 85.79 (80.78–90.80) 29.47 (22.93–36.02) Referent — 44.21 (37.08–51.34) Referent — 45.26 (38.12–52.41) Referent — 45.79 (38.64–52.94) Referent — Indigenous (n = 204) 88.24 (83.78–92.69) 32.35 (25.88–38.83) 1.14 (0.75–1.76) 0.537 42.16 (35.32–48.99) 0.92 (0.62–1.37) 0.681 42.16 (35.32–48.99) 0.88 (0.59–1.31) 0.535 41.78 (34.37–47.99) 0.83 (0.56–1.24) 0.356 Flock size ≤ 10000 (n = 166) 86.14 (80.83–91.46) 26.51 (19.72–33.29) Referent — 42.77 (35.17–50.38) Referent — 45.0 (21.11–68.88) Referent — 42.17 (34.58–49.76) Referent — > 10000 (n = 228) 87.72 (83.43–92.01) 34.21 (28.01–40.42) 1.44 (0.93–2.24) 0.103 43.42 (36.94–49.90) 1.03 (0.69–1.54) 0.898 43.33 (24.51–62.15) 1.11 (0.74–1.66) 0.612 44.30 (37.80-50.79) 1.09 (0.73–1.63) 0.674 Farming Multi-layer cage (n = 50) 94.0 (87.18–100) 50.0 (35.65–64.35) Referent — 40.0 (25.94–54.06) Referent — 42.17 (34.58–49.76) Referent — 48.0 (33.66–62.34) Referent — Ground floor (n = 344) 86.05 (82.37–89.73) 28.20 (23.42–32.98) 0.39 (0.22–0.72) 0.002 43.60 (38.34–48.87) 1.16 (0.63–2.12) 0.631 44.74 (38.23–51.24) 2.45 (1.26–4.76) 0.009 42.73 (37.48–47.99) 0.81 (0.45–1.47) 0.486 Drinking water Running water (n = 139) 86.33 (80.55–92.11) 23.02 (15.94–30.11) Referent — 51.80 (43.39–60.21) Referent — 46.76 (38.36–55.16) Referent — 36.69 (28.58–44.80) Referent — Groundwater (n = 255) 87.45 (83.36–91.54) 35.29 (29.39–41.20) 1.82 (1.14–2.92) 0.012 38.43 (32.42–44.44) 0.58 (0.38–0.88) 0.011 41.96 (35.86–48.06) 0.82 (0.54–1.25) 0.359 47.06 (40.89–53.23) 1.53 (1.0-2.34) 0.048 Control Vaccine (n = 38) 92.11 (83.12–100) 18.42 (5.51–31.33) Referent — 76.32 (62.15–90.48) 4.62 (2.06–10.38) < 0.001 50.0 (33.34–66.66) 1.51 (0.74–3.06) 0.255 34.21 (18.41–50.01) Referent — Drug (n = 188) 89.36 (84.91–93.81) 29.26 (22.69–35.82) 1.83 (0.76–4.41) 0.177 38.30 (31.29–45.31) 0.89 (0.58–1.36) 0.593 45.74 (38.56–52.93) 1.27 (0.83–1.94) 0.265 44.68 (37.51–51.85) 1.55 (0.75–3.22) 0.237 Combined (n = 168) 83.33 (77.64–89.03) 35.71 (28.39–43.03) 2.46 (1.02–5.92) 0.045 41.07 (33.56–48.59) Referent — 39.88 (32.40-47.36) Referent — 44.05 (36.46–51.63) 1.51 (0.73–3.16) 0.270 Necrotic enteritis No (n = 223) 79.82 (74.51–85.13) 22.87 (17.31–28.43) Referent — 38.12 (31.69–44.54) Referent — 33.63 (27.38–39.88) Referent — 38.57 (32.13-45.0) Referent — Yes (n = 171) 96.49 (93.71–99.28) 41.52 (34.06–48.98) 1.57 (1.05–2.36) 0.027 49.71 (42.14–57.28) 1.61 (1.07–2.40) 0.022 56.73 (49.22–64.23) 2.59 (1.72–3.90) < 0.001 49.71 (42.14–57.28) 1.57 (1.05–2.36) 0.027 Region Eastern (n = 87) 93.10 (87.67–98.54) 13.79 (6.40-21.19) Referent — 51.72 (41.01–62.44) Referent — 57.47 (46.87–68.07) 2.12 (1.20–3.74) 0.010 43.68 (33.05–54.31) Referent — Western (n = 70) 90.0 (82.80–97.20) 24.29 (13.99–34.58) 2.01 (0.88–4.54) 0.096 37.14 (25.54–48.75) 0.55 (0.29–1.05) 0.069 48.57 (36.57–60.57) 1.48 (0.81–2.71) 0.201 38.57 (26.88–50.26) 0.81 (0.43–1.54) 0.519 Northern (n = 124) 90.80 (84.61-97.0) 45.97 (37.07–54.86) 5.32 (2.63–10.75) < 0.001 39.52 (30.79–48.24) 0.61 (0.35–1.06) 0.080 35.48 (26.94–44.02) 0.86 (0.51–1.46) 0.583 42.74 (33.91–51.57) 0.96 (0.55–1.67) 0.892 Pearl River delta (n = 113) 80.0 (73.52–86.48) 31.86 (23.14–40.58) 2.92 (1.41–6.04) 0.004 44.25 (34.95–53.55) 0.74 (0.42–1.30) 0.294 38.94 (29.81–48.07) Referent — 46.90 (37.56–56.25) 1.14 (0.65-2.0) 0.650 Table 4 Multivariable logistic regression analysis of risk factors associated with prevalence of Eimeria in chickens in Guangdong province (n – total number of samples; 95% CI: 95% confidence interval; OR: odds ratios; significant predictors in bold) Variables Category Very high pathogenicity ( E. necatrix ) High pathogenicity ( E. tenella + E. brunetti ) Medium pathogenicity ( E. acervulina + E. maxima ) Low pathogenicity ( E.mitis + E. praecox ) Adjusted OR (95% CI) P -value Adjusted OR (95% CI) P -value Adjusted OR (95% CI) P -value Adjusted OR (95% CI) P -value Age Starter (n = 96) Referent — Referent — Referent — Referent — Grower ((n = 150) 2.12 (0.95–4.71) 0.066 1.50 (0.83–2.69) 0.177 0.72 (0.40–1.30) 0.278 2.90 (1.60–5.26) < 0.001 Adult (n = 148) 10.90 (4.91–24.19) < 0.001 1.37 (0.77–2.42) 0.280 0.84 (0.47–1.48) 0.538 1.99 (1.11–3.56) 0.021 Farming Multi-layer cage (n = 50) Referent — Referent — Referent — Referent — Ground floor (n = 344) 0.31 (0.12–0.82) 0.018 0.96 (0.41–2.24) 0.928 2.20 (0.89–5.44) 0.088 0.80 (0.34–1.85) 0.597 Drinking water Running water (n = 139) Referent — Referent — Referent — Referent — Groundwater (n = 255) 1.27 (0.61–2.63) 0.521 0.64 (0.35–1.18) 0.152 1.11 (0.61–2.02) 0.735 0.89 (0.49–1.62) 0.694 Control Vaccine (n = 38) Referent — 4.52 (1.21–16.91) 0.025 Referent — Referent — Drug (n = 188) 0.55 (0.12–2.48) 0.437 0.92 (0.47–1.80) 0.810 1.37 (0.49–3.83) 0.550 2.23 (0.79–6.31) 0.130 Combined (n = 168) 1.12 (0.20–6.37) 0.90 Referent — 1.26 (0.35–4.46) 0.723 2.55 (0.72–9.06) 0.149 Necrotic enteritis No (n = 223) Referent — Referent — Referent — Referent — Yes (n = 171) 3.22 (1.93–5.37) < 0.001 1.82 (1.19–2.78) 0.006 2.69 (1.76–4.11) < 0.001 1.57 (1.04–2.38 ) 0.033 Region Eastern (n = 87) Referent — Referent — Referent — Referent — Western ((n = 70) 2.57 (0.71–9.33) 0.151 0.94 (0.39–2.30) 0.895 0.59 (0.25–1.42) 0.243 0.56 (0.24–1.34) 0.194 Northern (n = 124) 4.14 (1.12–15.32) 0.034 1.36 (0.51–3.62) 0.537 0.42 (0.16–1.14) 0.088 0.65 (0.25–1.69) 0.374 Pearl River delta (n = 113) 3.29 (1.01–10.79) 0.049 1.63 (0.71–3.74) 0.253 0.43 (0.19-1.0) 0.051 0.81 (0.36–1.83) 0.616 Eimeria species profiles associated with necrotic enteritis occurrence The correlations between the infection of Eimeria species and the occurrence of NE were shown in Table 5 . According to univariate analysis, E. necatrix ( p < 0.001), E. tenella ( p < 0.05), E. brunetti ( p < 0.05), E. acervulina ( p < 0.001), E. maxima ( p < 0.05), and E. praecox ( p < 0.05) infection were associated with NE occurrence. Due to the observation of univariate analysis, E. mitis infection was not considered to the multivariate analysis. Logistic regression revealed increased risk of NE in flocks was highly associated with the infection of E. necatrix (OR = 2.20; 95% CI: 1.38–3.49; p < 0.001) and E. acervulina (OR = 2.37; 95% CI: 1.52–3.71; p < 0.001). Table 5 Univariable logistic regression analysis of risk factors associated with NE occurrence (95% CI: 95% confidence interval; OR: odds ratios; significant predictors in bold) Variables Category No. tested Positive (95% CI) OR (95% CI) P -value Adjusted OR (95% CI) P -value Eimeria necatrix No 272 36.76 (31.0-42.53) Referent — Referent — Yes 122 58.20 (49.32–67.07) 2.40 (1.55–3.70) < 0.001 2.20 (1.38–3.49) < 0.001 Eimeria tenella No 258 37.98 (32.02–43.95) Referent — Referent — Yes 136 53.68 (45.19–62.16) 1.89 (1.24–2.88) 0.003 1.32 (0.83–2.12) 0.245 Eimeria brunetti No 317 39.75 (34.33–45.16) Referent — Referent — Yes 77 58.44 (47.18–69.70) 2.13 (1.29–3.54) 0.003 1.49 (0.85–2.59) 0.162 Eimeria acervulina No 251 35.06 (29.12-41.0) Referent — Referent — Yes 143 58.04 (49.86–66.23) 2.56 (1.68–3.91) < 0.001 2.37 (1.52–3.71) < 0.001 Eimeria maxima No 322 40.68 (35.29–46.08) Referent — Referent — Yes 72 55.56 (43.80-67.31) 1.82 (1.09–3.05) 0.022 1.14 (0.64–2.05) 0.654 Eimeria mitis No 256 41.41 (35.33–47.48) Referent — — — Yes 138 47.10 (38.67–55.53) 1.26 (0.83–1.91) 0.277 — — Eimeria praecox No 329 40.73 (35.39–46.07) Referent — Referent — Yes 65 56.92 (44.56–69.29) 1.92 (1.12–3.29) 0.017 1.42 (0.79–2.56) 0.241 Discussion Coccidiosis represents a significant economical challenge for the global poultry industry. This study investigated the prevalence of Eimeria species in Guangdong province, filling a critical research gap [ 13 ]. The overall prevalence of coccidiosis in Guangdong (87.06%; 343/394) was higher than that in Zhejiang province in China (30.7%; 95/310) [ 14 ], Shandong province in China (65.8%; 50/76) [ 15 ], Korean (75%; 291/388) [ 16 ], Serbia (59%; 59/100) [ 17 ], north India (28.5%; 171/600) [ 18 ], and southwestern Nigeria (41.3%; 2292/5544) [ 19 ]. The heightened prevalence in Guangdong province could be attributed to the climatic conditions, characterized by increased temperature and humidity, which promote the propagation of Eimeria in broiler flocks. Our finding aligned more closely with previous reports from other tropical and subtropical regions and countries, including Anhui province in China (87.75%; 150/171) [ 20 ], two northern Indian states (81.03%; 47/58) [ 21 ] and Greece (85.7%; 36/42) [ 22 ]. Conversely, higher prevalence rates were documented in Henan province (96.70%; 176/182) and Hubei province in China (97.79%; 133/136) [ 23 ], Colombia (96.3%; 236/245) [ 24 ], Australia (98%; 255/260) [ 25 ], Japan (91.9%; 33/37) [ 26 ], and northeastern Algeria (99.5%; 186/187) [ 27 ]. This variability could be attributed to differing climate conditions, seasonal variations, different terrains and management practices in different regions and countries. Seven distinct Eimeria species were identified within broiler chicken farms in Guangdong province, with the most prevalent species being E. acervulina (36.29%), E. mitis (35.03%), E. tenella (34.52%), and E. necatrix (30.96%). It is well-known that the interactions between Eimeria species and crowing effects play a pivotal role in oocyst production [ 28 ]. E. acervulina and E. tenella exhibit higher productive potential, and in cases of mixed infection, E. acervulina tends to suppress the oocyst production of E. necatrix , E. maxima and E. brunetti [ 29 , 30 ]. Our study found that single-species infections was predominant (42.86%), and only 39.94% of samples contained two to three Eimeria species within a single fecal sample. The most prevalent combinations were E. acervulina – E. tenella (15.48%), followed by E. acervulina – E. necatrix (14.21%), aligning with findings from various regions worldwide, including Colombia [ 24 ], Iran [ 31 ], India [ 32 ], and Alegeria [ 33 ]. Multivariable logistic regression analysis identified several potential risk factors associated with Eimeria species prevalence, with bird age emerging as the most important factor. Our results indicated that flocks with adult chickens faced the highest risk of E. necatrix infection (OR = 11.13, 95% CI: 4.98–24.89; p < 0.001). This finding is consistent with the previous reports suggesting higher prevalence rates among adult birds compared to birds in other ages [ 34 , 35 ]. However, it contrasts with studies by Lawal et al. [ 36 ] and Khursheed et al. [ 18 ], which reported that younger birds were more susceptible to infection than older birds. This discrepancy might be attributed to variations in the mainly prevalence of Eimeria species. E. necatrix is known to possess lower reproductive capabilities and as a ‘poor competitor’ compared to other species, hence being more commonly detected in older birds [ 37 ]. Notably, outbreaks due to E. necatrix predominantly occur in older birds aged 9–14 weeks [ 38 ]. The increase of epidemic E. necatrix prevalence observed in this study underscores the importance of improving preventative measures. In the present study, E. necatrix (OR = 2.20, 95% CI: 1.38–3.49; p < 0.001) and E. acervulina (OR = 2.37, 95% CI: 1.52–3.71; p < 0.001) infections were highly associated with NE occurrence. Similarly, previous study found that Eimeria species infection rates was significantly associated with the history of clostridiosis in farms (OR = 2.6, 95% CI: 1.19–2.78; p = 0.006) [ 31 ]. Previous studies have shown that Eimeria infection is a major predisposing factor for NE [ 39 ]. Coccidia-induced damage to the intestinal epithelium creates an environment allowing rapid replication and toxin production of Clostridium perfringens [ 40 ]. As a result, Eimeria species, particularly E. acervulina , E. maxima and E. necatrix , are often used experimentally in conjunction with C. perfringens to induce NE. In this study, NE was significantly associated with both E. necatrix and E. acervulina . However, there is some debate over whether the more virulent Eimeria , such as E. necatrix are superior to the less virulent ones like E. acervulina in terms of predisposing bird to NE [ 39 ]. Factors including geographical locations, control methods used, and management type displayed association with a higher prevalence of coccidia, althoght these association were weaker. The association between geographical variation and elevated prevalence of coccidia has been reported in previous studies [ 32 , 41 , 42 ]. In this study, the risk factor of E. necatrix occurrence was 4.14 times (95% CI: 1.12–15.32; p < 0.05) and 3.29 times (95% CI: 1.01–10.79; p < 0.05) higher in northern Guangdong and Peal River delta, respectively, compare to eastern Guangdong. This might be attributed to heavier rainfall (approximately 149 mm/year) and relatively lower humidity (approximately 57–64%) in both northern Guangdong and Peal River delta. These findings are consistent with those reported by Waldenstedt et al. [ 43 ] that the sporulation of E. maxima oocysts was poorest under the conditions of high moisture content of 62%, suggesting that oocyst sporulation may be more efficient in drier litter [ 37 ]. In the present study, a high risk of E. tenella – E. brunetti infection in vaccinated flocks was identified compared to those administrated a combination of vaccines and anticoccidials (OR = 4.52, 95% CI: 1.21–16.91; p < 0.05). This study also found a high prevalence of E. brunetti (19.54%) in Guangdong, compared with previous study (6.6%) in China [ 23 ], for which no commercial vaccines containing E. brunetti available in China. Given its classification as a highly pathogenic species, the inclusion of E. brunetti in vaccines in China may be warranted. Furthermore, previous studies found that chickens raised by free-range type of management had a higher coccidiosis occurrence compared to those raised by cages [ 16 , 23 ], since the transmission of sporulated oocysts of coccidia is mainly through the fecal-oral route. However, in this study, a high risk of E. necatrix infection was also found in multi-layer caged flocks, highlighting the necessity of vaccination in coccidiosis prevention in modern poultry farm. Coccidia prevalence in birds raised in multi-layer cages is high may be attributed to high bird density and suboptimal cage design or maintenance. Further studies with larger sample size are warranted to explore the prevalence of Eimeria in flocks administrated different farming methods. Conclusion This study underscores the substantial prevalence of Eimeria species infections in chickens across Guangdong province, China. The predominant species identified were E. acervuline, E. mitis , E. tenella , and E. necatrix . The analysis of risk factors showed that NE occurrence, control methods, geographical variation, and bird age were associated with Eimeria species infection in chickens. In addition, the findings herein have shed the light on the necessity of considering E. brunetti , a high pathogenetic strain, for inclusion in vaccines in China. In light of the identified risk factors, it is imperative to implement effective control strategies and management practices to reduce infections and subsequent economic losses in poultry farming. Methods Study area and farms The study was conducted across four distinct regions, geographically spanning between 20°09'–25°31' north latitude and 109°45'–117°20' east longitude located in south China, covering a total land area of 179,800 km 2 . The study was carried out over an extensive timeframe, spanning from April 2020 to November 2021. The climate in Guangdong is subtropical with mild winters and hot, muggy summers. The annual average temperature was about 23–25°C. Furthermore, the annual averages relative humidity levels ranged from 57–77%. The average monthly rainfall was approximately from 118 mm to 150 mm, drawing data from https://www.worldweatheronline.com/ as the source (Table 6 ). Samples were collected from 89 broiler farms (21 from eastern Guangdong, 19 from western Guangdong, 18 from northern Guangdong, and 31 from Peal River delta) (Fig. 2 ). Each farm had 2–20 houses, accommodating bird populations ranging from 5000 to 40000 individuals at a density of 10–16 birds/m 2 . The predominant broiler breeds are three-yellow chicken and spotted-brown chicken. The bedding materials in use were wood shavings or rice husk. A questionnaire was administered at each farm in parallel with fecal sample collection. The questionnaire included questions pertaining to key factors such as bird age, bird breed, flock size, farming method, source of drinking water, and control methods employed. Table 6 Management of characteristics of broiler farms in four regions of Guangdong, China during 2020 to 2021. Variables Eastern Western Northern Peal River delta Annual average temperature (°C) 23.04 24.67 24.0 24.63 Annual average humidity (%) 73.08 76.75 64.17 57.88 Annual average rainfall (mm) 118.69 119.80 149.50 149.81 Genetic line in number of sampled farms a Indigenous 12 13 12 12 Indigenous CBs 9 6 6 19 Litter composition wood shavings wood shavings wood shavings/ rice husk wood shavings/ rice husk Type of farming ground floor ground floor ground floor/ multi-layer cage ground floor/ multi-layer cage Type of drinking water running water/ groundwater running water/ groundwater running water/ groundwater running water/ groundwater Average age of birds at sampling (min. to max.) 45 (20–65) 51 (23–90) 49 (22–79) 45 (17–86) Flock size (min. to max.) 11667 (5000–20000) 14975 (9000–20000) 17341 (7000–40000) 11669 (8000–23000) a 89 total surveys. Fecal sample collection and analysis Fresh fecal samples were collected from various locations within each poultry house. Each sample weighed approximately 250 g consisted of 30 fresh fecal droppings gathered from a single house. A total of 394 fecal samples were collected from 89 farms and were placed in labelled zipped plastic bags. The samples were transported immediately to the laboratory and stored at 4°C until using. All the collected samples underwent microscopic examination for the presence of Eimeria oocysts, as previously described [ 44 ]. Moreover, PCR was employed to identify the specific Eimeria species within the fecal samples. Genomic DNA extraction Before the DNA extraction, each sample was mixed with an equal volume of sterile ddH 2 O and was homogenized using a blender. Subsequently, aliquots of 200 µl of these prepared samples were transferred into a 1.5 ml Eppendorf tube for DNA extraction. Genomic DNA extraction was performed using the E.Z.N.A.® Stool DNA Kit according to the manufacturer’s protocol (Omega, D4015). The DNA was stored at -20°C until using. Molecular characterization of Eimeria species Eimeria species were identified by PCR using species-specific primers (Table 7 ) as previously described by Schnitzler et al. [ 45 , 46 ] and Haug et al. [ 10 ]. For each sample, a total volume of 20 µl was mixed, which includes 10 µl of Premix Taq ™ (Takara, RR901A), 500 nm of species-specific for forward and reverse primers, 2 µl of DNA sample and 6 µl of ddH 2 O. The amplification was performed using T100™ thermal cycler (Bio-Rad, USA) following cycling condition of an initial denaturation step at 95°C for 2 min, followed by 35 repeat cycles, each consisting of 30 seconds of denaturation at 95°C, 30 seconds of annealing at 62°C and a 1 minute of extension step at 72°C with a final extension continued for 3 minutes. The PCR products were determined by electrophoresis on 1.5% agarose gels. Table 7 PCR primers for the seven chicken Eimeria species. Eimeria species Primer name Primer sequences 5′ – 3′ Annealing temperature (°C) Expected amplicon size (bp) Eimeria necatrix ENF a TACATCCCAATCTTTGAATCG 61 383 ENR a GGCATACTAGCTTCGAGCAAC Eimeria tenella ETF a AATTTAGTCCATCGCAACCCT 60 271 ETR a CGAGCGCTCTGCATACGACA Eimeria brunetti EBF a GATCAGTTTGAGCAAACCTTCG 45 310 EBR a TGGTCTTCCGTACGTCGGAT Eimeria acervulina EAF a GGCTTGGATGATGTTTGCTG 60 321 EAR a CGAACGCAATAACACACGCT Eimeria maxima EmuF b GTGGGACTGTGGTGATGGGG 60 162 EmuR b ACCAGCATGCGCTCACAACCC Eimeria mitis EMIF c TATTTCCTGTCGTCGTCTCGC 54 306 EMIR c GTATGCAAGAGAGAATCGGGA Eimeria praecox EPF c CATCATCGGAATGGCTTTTTGA 54 368 EPR c AATAAATAGCGCAAAATTAAGCA a Primers described by Schnitzler et al. (1998) b Primers described by Haug et al. (2007) c Primers described by Schnitzler et al. (1999) Molecular identification of Clostridium perfringen The identification of Clostridium perfringen in fecal samples was conducted using quantitative real-time PCR (qPCR) targeting the alpha toxin gene, as described by Mohiuddin et al. [ 47 ]. The qPCR was carried out in a reaction mixture of 20 ul, containing TB Green Premix Ex Taq II (Takara, RR820B) (10 µL), forward primers (1 µL), reverse primers (1 µL), template DNA 1µL (150–200 ng), and ddH 2 O (7 µL). The amplification process was performed using CFX Connect™ Real-Time PCR System (Bio-Rad, USA). The amplification program was at 95°C for 30 s, 35 cycles of denaturation at 95°C for 15 s, annealing at 60°C for 30 s, and a final step for dissociation at 95°C for 10 s, 65°C for 5 s, and 95°C for 5 s. Statistical analysis All statistical analyses were performed using software IBM SPSS Statistics 27.0 (SPSS Inc., http://www.spss.com.hk ). Descriptive statistics including bird age, bird breed, flock size, farming type, type of drinking water, control strategy, and presence of NE were obtained from the questionnaires. Initially, the prevalence of Eimeria spp. infections with a 95% confidence interval (CI) was calculated. Then, the predictor variables associated with the presence of different type of Eimeria spp. were assessed using univariable and multivariable logistic regression models. Multivariable models were built by using forward stepwise logistic regression procedures (with inclusion if p < 0.05). Chi-square test or Fisher’s exact test was used to compare the prevalence of one or more Eimeria infection in variables according to age, breed, flock size, farming type, drinking water source, control strategy, region and presence of NE and the odds ratios (OR, with 95% CI) were calculated to assess the associations between participants’ characteristics and Eimeria species infection. Data with p values ≤ 0.05 were considered as statistical significance. Abbreviations E. necatrix Eimeria necatrix E. tenella Eimeria tenella E. brunetti Eimeria brunetti E. acervulina Eimeria acervulina E. maxima Eimeria maxima E. mitis Eimeria mitis E. praecox Eimeria praecox NE necrotic enteritis PCR Polymerase chain reaction qPCR Quantitative real-time polymerase chain reaction ITS internal transcribed spacer SCAR sequence characterized amplified region OR odds ratios CI confidence interval Declarations Authors’ contributions SL, NQ, and MS designed this study. SL, XL, QZ and ZY collected samples. CW, JL, ML, JH, HC, YS, and XC performed experiments. SL, XL, YZ, LY, JZ, NQ, and MS interpreted the results and drafted the manuscript. All authors have read and approved the final version of the manuscript. Funding This work was supported by the National Key Research and Development Program of China (2023YFD1801202) , The open competition program of top ten critical priorities of Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province (2023SDZG02), Key Realm R&D Program of Guangdong Province (2023B0202150001), Opening Project of State Key Laboratory of Swine and Poultry Breeding Industry (2023QZ-NK05, 2022GZ07), Science and technology project of Yunfu (2022020202), Science and technology project of Guangzhou (2023B04J0137, 2023A04J0789), Special fund for scientific innovation strategy-construction of high level Academy of Agriculture Science (202110TD, 202122TD, R2020PY-JC001, R2019YJ-YB3010, R2020PY-JG013, R2020QD-048, R2021PY-QY007, R2023PY-JG018), The Project of Collaborative Innovation Center of GDAAS (XTXM202202). Data availability All data are available upon request. Acknowledgements Not applicable. 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Junjing","middleName":"","lastName":"Hu","suffix":""},{"id":268984916,"identity":"f133d12f-135f-4d06-9ed6-ea0f5ff67eda","order_by":8,"name":"Haiming Cai","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Haiming","middleName":"","lastName":"Cai","suffix":""},{"id":268984917,"identity":"ed5cfa47-40bf-49b2-b7e3-8792ea9c175c","order_by":9,"name":"Yongle Song","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yongle","middleName":"","lastName":"Song","suffix":""},{"id":268984918,"identity":"e375c886-e6a7-4f8e-9544-52f882167f9c","order_by":10,"name":"Xiangjie Chen","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiangjie","middleName":"","lastName":"Chen","suffix":""},{"id":268984919,"identity":"9128510a-9371-4f8c-af74-8a43fbb0c3e1","order_by":11,"name":"Yibin Zhu","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yibin","middleName":"","lastName":"Zhu","suffix":""},{"id":268984920,"identity":"5f0c2a5d-1553-4923-af06-8ec7212d87ac","order_by":12,"name":"Lijun Yin","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Yin","suffix":""},{"id":268984921,"identity":"452ecb6d-396a-4784-9522-6384a6c17b74","order_by":13,"name":"Jianfei Zhang","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jianfei","middleName":"","lastName":"Zhang","suffix":""},{"id":268984922,"identity":"eec33314-e7fc-438e-a8f9-c2a11b450f39","order_by":14,"name":"Nanshan Qi","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Nanshan","middleName":"","lastName":"Qi","suffix":""},{"id":268984923,"identity":"e3e849e0-fb32-4520-88b8-f15feac391f2","order_by":15,"name":"Mingfei Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYLCCBAYGfgZm5gMMPKRokWxgZksgQQsDSAsDjwFxWuRn5B7d8HBHrQQ/O8/nF29q7OwZ2M8ewKvF4EZe2o3EM8clJJt5t1nOOZac2MCTl4Bfi0SO2Y3EtmN1Bod5txnzsDEnMEgAXYjfYRAtEvaHeZ4Z8/yrtyeoheEGWEuNhAEzD/Nj3rbDjA2EtBiceQPSckBC4jCbGePcvuOJbTw5BBzWnmN282dbnQR//+HHH958q7bnZz9DwGEQcBhEsEmASWLUA0EdiGD+QKTqUTAKRsEoGGEAADFrRFm+DaSaAAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mingfei","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-01-23 06:59:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3890180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3890180/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12917-024-03990-4","type":"published","date":"2024-05-03T19:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50136945,"identity":"ced01ac4-1495-4f2b-94bb-95a9e37d512a","added_by":"auto","created_at":"2024-01-25 05:40:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108557,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of mixed infections in broiler chickens from Guangdong province, China.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3890180/v1/d127638848df512863a9c4c8.png"},{"id":50136946,"identity":"a7806fe3-1710-4738-a43b-787b4053182f","added_by":"auto","created_at":"2024-01-25 05:40:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":462580,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic location of the sampling sites in Guangdong province. Eastern, Western, Northern, and Pearl River delta of Guangdong are shaded as indicated.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3890180/v1/8180377f1d86182691eb9fae.png"},{"id":56043018,"identity":"83c5b474-c69b-4a7a-8e4f-1b7ac5a1abfd","added_by":"auto","created_at":"2024-05-07 20:09:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2342893,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3890180/v1/a59df389-3771-46c1-afc5-813331dc7f7d.pdf"},{"id":50136947,"identity":"2d2bf198-0c26-458e-a300-969275205ca6","added_by":"auto","created_at":"2024-01-25 05:40:08","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":827501,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-3890180/v1/4baba74c275b9354b276f54c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and geographic distribution of Eimeria species on commercial broiler farms in Guangdong, China","fulltext":[{"header":"Background","content":"\u003cp\u003eCoccidiosis ranks among the foremost and widespread disease affecting chickens worldwide. Triggering by protozoan parasites belonging to the \u003cem\u003eEimeria\u003c/em\u003e genus, coccidiosis inflicts severe damage to the intestinal tract, resulting in highened mortality rates, reduced weight gain, impaired nutrient absorption, and increased susceptibility to other enteric pathogens [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The far-reaching repercussions of this disease translate into a profound economic impact on poultry industry [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In chickens, seven species, namely, \u003cem\u003eE. tenella\u003c/em\u003e, \u003cem\u003eE. necatrix\u003c/em\u003e, \u003cem\u003eE. brunetti\u003c/em\u003e, \u003cem\u003eE. acervulina\u003c/em\u003e, \u003cem\u003eE. maxima\u003c/em\u003e, \u003cem\u003eE. mitis\u003c/em\u003e, and \u003cem\u003eE. praecox\u003c/em\u003e have been recognized, each with a proclivity for specific segments of the intestinal tract and exhibiting different pathogenicity, yielding distinct clinical manifestations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. \u003cem\u003eE. necatrix\u003c/em\u003e, for instance, emerges as the most pathogenic species, alongside the relatively prevalent \u003cem\u003eE. tenella\u003c/em\u003e, both induce bloody lesions and give rise to elevated morbidity and mortality rates in chickens [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; \u003cem\u003eE. brunetti\u003c/em\u003e exhibits highly pathogenic and is associated with haemorrhagic coccidiosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; Conversely, \u003cem\u003eE. acervulina\u003c/em\u003e and \u003cem\u003eE. maxima\u003c/em\u003e are classifies as moderately pathogenic, provoking inflammation of the intestinal wall characterized by pinpoint haemorrhage and epithelial demolition [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Finally, \u003cem\u003eE. mitis\u003c/em\u003e, and \u003cem\u003eE. praecox\u003c/em\u003e are generally considered less pathogenic, causing malabsorption and enteritis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eControl strategies mainly based upon chemotherapy or vaccination. However, the development of drug resistance over time in different parts of the world and the lack of new anticoccidial drugs have reduced efficacy of anticoccidial agents [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Anticoccidial live vaccines have been used to prevent coccidiosis since last decades [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. There are three types of live anticoccidial vaccines currently available for use in China, including trivalent vaccine containing \u003cem\u003eE. tenella\u003c/em\u003e, \u003cem\u003eE. acervulina\u003c/em\u003e and \u003cem\u003eE. maxima\u003c/em\u003e; tetravalent vaccine containing \u003cem\u003eE. tenella\u003c/em\u003e, \u003cem\u003eE. necatrix\u003c/em\u003e, \u003cem\u003eE. acervulina\u003c/em\u003e and \u003cem\u003eE. maxima\u003c/em\u003e); and one importing vaccine Coccivac\u0026trade; containing \u003cem\u003eE. maxima\u003c/em\u003e, \u003cem\u003eE. mivati\u003c/em\u003e, \u003cem\u003eE. acervulina\u003c/em\u003e and \u003cem\u003eE. tenella\u003c/em\u003e. To estimate the efficiency of the current control strategies, including the composition of vaccines, it is essential to understand the epidemiology of \u003cem\u003eEimeria\u003c/em\u003e species, as well as potential risk factors associated with occurrence of different \u003cem\u003eEimeria\u003c/em\u003e species.\u003c/p\u003e \u003cp\u003eThe conventional taxonomy of \u003cem\u003eEimeria\u003c/em\u003e species has relied on morphological attributes, the segments of the intestinal tract affected and the pre-patent period of the \u003cem\u003eEimeria\u003c/em\u003e following \u003cem\u003ein vivo\u003c/em\u003e infection in chickens [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Nevertheless, these conventional methods may fall short of achieving precise diagnosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In recent times, polymerase chain reaction (PCR) techniques have emerged as a valuable tool for the identification of all seven \u003cem\u003eEimeria\u003c/em\u003e species. This molecular method employs genetic markers situated within the internal transcribed spacer-1 (ITS-1), ITS-2, and the sequence characterized amplified region (SCAR) [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As of now, there is a conspicuous absence of accurate data or previously reported information concerning the prevalence of \u003cem\u003eEimeria\u003c/em\u003e species in broiler chicken farms in Guangdong province, China. Therefore, the purpose of this study is to investigate the epidemiology of \u003cem\u003eEimeria\u003c/em\u003e species in Guangdong province and simultaneously analyze the risk factors associated with the presence of different \u003cem\u003eEimeria\u003c/em\u003e species. The finding from this study will not only contribute to our understanding of the occurrence and potential control strategies for coccidiosis in poultry in Guangdong province, China, but also enhance our comprehension of the potential risk factors associated with intensive poultry management practices.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eEimeria\u003c/strong\u003e \u003cstrong\u003especies occurrence in different regions in Guangdong\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we observed a 87.06% (343/394) prevalence of \u003cem\u003eEimeria\u003c/em\u003e species in Guangdong. All \u003cem\u003eEimeria\u003c/em\u003e species were detected in four regions of Guangdong (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), with \u003cem\u003eE. acervulina\u003c/em\u003e (36.29%), \u003cem\u003eE. mitis\u003c/em\u003e (35.03%), \u003cem\u003eE. tenella\u003c/em\u003e (34.52%) and \u003cem\u003eE. necatrix\u003c/em\u003e (30.96%) emerged as the predominant species. Geographically, \u003cem\u003eE. necatrix\u003c/em\u003e had a wider distribution in northern Guangdong and Pearl River delta (45.97% and 31.86%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while \u003cem\u003eE. acervulina\u003c/em\u003e was more prevalent in eastern and western Guangdong (47.13% and 44.29%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Single-species infections were common across all four regions of Guangdong (42.86%). Additionally, 39.94% of samples contained 2 to 3 \u003cem\u003eEimeria\u003c/em\u003e species in a single fecal sample (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, the most prevalent combinations across the four regions were \u003cem\u003eE. acervulina\u003c/em\u003e\u0026ndash;\u003cem\u003eE. tenella\u003c/em\u003e (15.48%), followed by \u003cem\u003eE. acervulina\u003c/em\u003e\u0026ndash;\u003cem\u003eE. necatrix\u003c/em\u003e (14.21%), \u003cem\u003eE. acervulina\u003c/em\u003e\u0026ndash;\u003cem\u003eE. tenella\u003c/em\u003e\u0026ndash;\u003cem\u003eE. mitis\u003c/em\u003e (6.85%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and \u003cem\u003eE. acervulina\u003c/em\u003e\u0026ndash;\u003cem\u003eE. tenella\u003c/em\u003e\u0026ndash;\u003cem\u003eE. necatrix\u003c/em\u003e (6.60%). Specifically, the prevalence of both \u003cem\u003eE. acervulina\u003c/em\u003e\u0026ndash;\u003cem\u003eE. tenella\u003c/em\u003e (22.99%) and \u003cem\u003eE. acervulina\u003c/em\u003e\u0026ndash;\u003cem\u003eE. tenella- E. mitis\u003c/em\u003e (13.79%) was highest in eastern regions, while both \u003cem\u003eE. acervulina\u003c/em\u003e\u0026ndash;\u003cem\u003eE. necatrix\u003c/em\u003e (19.47%) and \u003cem\u003eE. acervulina\u003c/em\u003e\u0026ndash;\u003cem\u003eE. tenella- E. necatrix\u003c/em\u003e (8.85%) were most prevalent in Pearl River delta (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrevalence of \u003cem\u003eEimeria\u003c/em\u003e infection in broiler chickens by studied regions of Guangdong province over 2020\u0026ndash;2021 (n \u0026ndash; total number of samples; 95% CI: 95% confidence interval; significant predictors in bold)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 13.3838%;\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria\u003c/em\u003e species\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 15.1515%;\"\u003e\n \u003cp\u003ePathogenicity group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;394)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003eEastern (n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eWestern (n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.8788%;\"\u003e\n \u003cp\u003eNorthern (n\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 15.5303%;\"\u003e\n \u003cp\u003ePearl River delta (n\u0026thinsp;=\u0026thinsp;113)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 6.0606%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.8788%;\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 15.5303%;\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3838%;\"\u003e\n \u003cp\u003eAny \u003cem\u003eEimeria\u003c/em\u003e species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1515%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e87.06 (83.73\u0026ndash;90.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e93.10 (87.67\u0026ndash;98.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e90.0 (82.80\u0026ndash;97.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.8788%;\"\u003e\n \u003cp\u003e86.29 (80.15\u0026ndash;92.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 15.5303%;\"\u003e\n \u003cp\u003e81.42 (74.13\u0026ndash;88.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.0606%;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3838%;\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria necatrix\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1515%;\"\u003e\n \u003cp\u003eVery high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e30.96 (26.38\u0026ndash;35.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e13.79 (6.40-21.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e24.29 (13.99\u0026ndash;34.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.8788%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e45.97 (37.07\u0026ndash;54.86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 15.5303%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31.86 (23.14\u0026ndash;40.58)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.0606%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3838%;\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria tenella\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1515%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e34.52 (29.80-39.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e45.98 (35.29\u0026ndash;56.66)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e25.71 (15.22\u0026ndash;36.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.8788%;\"\u003e\n \u003cp\u003e31.45 (23.16\u0026ndash;39.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 15.5303%;\"\u003e\n \u003cp\u003e34.51 (25.61\u0026ndash;43.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.0606%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3838%;\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria brunetti\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1515%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e19.54 (15.61\u0026ndash;23.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e21.84 (12.98\u0026ndash;30.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e17.14 (8.09\u0026ndash;26.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.8788%;\"\u003e\n \u003cp\u003e16.94 (10.24\u0026ndash;23.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 15.5303%;\"\u003e\n \u003cp\u003e22.12 (14.35\u0026ndash;29.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.0606%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3838%;\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria acervulina\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1515%;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e36.29 (31.53\u0026ndash;41.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e47.13 (36.43\u0026ndash;57.83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e44.29 (32.36\u0026ndash;56.22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.8788%;\"\u003e\n \u003cp\u003e25.0 (17.27\u0026ndash;32.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 15.5303%;\"\u003e\n \u003cp\u003e35.40 (26.45\u0026ndash;44.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.0606%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3838%;\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria maxima\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1515%;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e18.27 (14.44\u0026ndash;22.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e27.59 (18.01\u0026ndash;37.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e15.71 (6.97\u0026ndash;24.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.8788%;\"\u003e\n \u003cp\u003e17.74 (10.92\u0026ndash;24.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 15.5303%;\"\u003e\n \u003cp\u003e13.27 (6.92\u0026ndash;19.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.0606%;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3838%;\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria mitis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1515%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e35.03 (30.29\u0026ndash;39.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e34.48 (24.29\u0026ndash;44.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e31.43 (20.28\u0026ndash;42.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.8788%;\"\u003e\n \u003cp\u003e37.10 (28.48\u0026ndash;45.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 15.5303%;\"\u003e\n \u003cp\u003e35.40 (26.45\u0026ndash;44.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.0606%;\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3838%;\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria praecox\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1515%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e16.50 (12.82\u0026ndash;20.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.2475%;\"\u003e\n \u003cp\u003e24.14 (14.96\u0026ndash;33.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e10.0 (2.80\u0026ndash;17.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.8788%;\"\u003e\n \u003cp\u003e12.90 (6.92\u0026ndash;18.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 15.5303%;\"\u003e\n \u003cp\u003e18.58 (11.30-25.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.0606%;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrevalence of mixed infections in broiler chickens from Guangdong province (n \u0026ndash; total number of samples; 95% CI: 95% confidence interval; significant predictors in bold)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 7.0372%;\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria\u003c/em\u003e species\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.1199%;\"\u003e\n \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;586)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.1199%;\"\u003e\n \u003cp\u003eEastern (n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 7.7139%;\"\u003e\n \u003cp\u003eWestern (n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.2552%;\"\u003e\n \u003cp\u003eNorthern (n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.8265%;\"\u003e\n \u003cp\u003ePearl River delta (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 3.5186%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 8.1199%;\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.1199%;\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 7.7139%;\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.2552%;\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.8265%;\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.0372%;\"\u003e\n \u003cp\u003eEA\u0026ndash;ET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.1199%;\"\u003e\n \u003cp\u003e15.48 (11.89\u0026ndash;19.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.1199%;\"\u003e\n \u003cp\u003e22.99 (13.97\u0026ndash;32.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.7139%;\"\u003e\n \u003cp\u003e15.71 (6.97\u0026ndash;24.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2552%;\"\u003e\n \u003cp\u003e9.68 (4.40-14.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.8265%;\"\u003e\n \u003cp\u003e15.93 (9.08\u0026ndash;22.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 3.5186%;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.0372%;\"\u003e\n \u003cp\u003eEA\u0026ndash;EN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.1199%;\"\u003e\n \u003cp\u003e14.21 (10.75\u0026ndash;17.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.1199%;\"\u003e\n \u003cp\u003e9.20 (3.0-15.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.7139%;\"\u003e\n \u003cp\u003e7.14 (0.96\u0026ndash;13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2552%;\"\u003e\n \u003cp\u003e16.94 (10.24\u0026ndash;23.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.8265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.47 (12.66\u0026ndash;26.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 3.5186%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.0372%;\"\u003e\n \u003cp\u003eEA\u0026ndash;ET\u0026ndash;EN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.1199%;\"\u003e\n \u003cp\u003e6.60 (4.14\u0026ndash;9.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.1199%;\"\u003e\n \u003cp\u003e6.90 (1.46\u0026ndash;12.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.7139%;\"\u003e\n \u003cp\u003e1.43 (0-4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2552%;\"\u003e\n \u003cp\u003e7.26 (2.63\u0026ndash;11.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.8265%;\"\u003e\n \u003cp\u003e8.85 (3.53\u0026ndash;14.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 3.5186%;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.0372%;\"\u003e\n \u003cp\u003eEA\u0026ndash;ET\u0026ndash;EMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.1199%;\"\u003e\n \u003cp\u003e6.85 (4.34\u0026ndash;9.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.1199%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.79 (6.40-21.19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.7139%;\"\u003e\n \u003cp\u003e1.43 (0-4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2552%;\"\u003e\n \u003cp\u003e4.84 (1.01\u0026ndash;8.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.8265%;\"\u003e\n \u003cp\u003e7.08 (2.28\u0026ndash;11.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 3.5186%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 54.0648%;\"\u003e\u003csup\u003ea\u003c/sup\u003e \u003cem\u003eEimeria\u003c/em\u003e species: \u003cem\u003eE. acervulina\u003c/em\u003e (EA), \u003cem\u003eE. tenella\u003c/em\u003e (ET), \u003cem\u003eE. necatrix\u003c/em\u003e (EN), \u003cem\u003eE. mitis\u003c/em\u003e (EMI).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk factors associated with\u003c/strong\u003e \u003cstrong\u003eEimeria\u003c/strong\u003e \u003cstrong\u003especies occurrence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis revealed significant associations between the prevalence of \u003cem\u003eEimeria\u003c/em\u003e species and several variables, including bird age, management type, drinking water source, control strategy, presence of NE, and location of farm (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Bird breed and flock size, however, did not considered to the multivariate analysis. Logistic regression identified several key risk factors associated with \u003cem\u003eE. necatrix\u003c/em\u003e infection, with adult birds (OR\u0026thinsp;=\u0026thinsp;10.90; 95% CI: 4.91\u0026ndash;24.19; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), NE (OR\u0026thinsp;=\u0026thinsp;3.22; 95% CI: 1.93\u0026ndash;5.37; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), flocks from northern Guangdong (OR\u0026thinsp;=\u0026thinsp;4.14; 95% CI: 1.12\u0026ndash;15.32; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Peal River delta (OR\u0026thinsp;=\u0026thinsp;3.29; 95% CI: 1.01\u0026ndash;10.79; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) demonstrated a notably higher risk. Likewise, flocks vaccinated with anticoccidial live vaccines (OR\u0026thinsp;=\u0026thinsp;4.52; 95% CI: 1.21\u0026ndash;16.91; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and presence of NE (OR\u0026thinsp;=\u0026thinsp;1.82; 95% CI: 1.19\u0026ndash;2.78; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were positively associated with a higher likelihood of \u003cem\u003eE. tenella\u003c/em\u003e\u0026ndash;\u003cem\u003eE. brunetti\u003c/em\u003e infection. Flocks with the presence of NE (OR\u0026thinsp;=\u0026thinsp;2.69; 95% CI: 1.76\u0026ndash;4.11; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also associated with higher prevalence of \u003cem\u003eE. acervulina\u003c/em\u003e\u0026ndash;\u003cem\u003eE. maxima\u003c/em\u003e infection. Furthermore, our results indicated that flocks with grower birds (OR\u0026thinsp;=\u0026thinsp;2.90; 95% CI: 1.60\u0026ndash;5.26; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), adult chickens (OR\u0026thinsp;=\u0026thinsp;1.99; 95% CI: 1.11\u0026ndash;3.56; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), along with the occurrence of NE (OR\u0026thinsp;=\u0026thinsp;1.57; 95% CI: 1.04\u0026ndash;2.38; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), had an increased likelihood of \u003cem\u003eE. mitis\u003c/em\u003e\u0026ndash;\u003cem\u003eE. praecox\u003c/em\u003e infection. Bird breed and flock size were not statistically significant associated with \u003cem\u003eEimeria\u003c/em\u003e infection (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariable logistic regression analysis of risk factors associated with prevalence of \u003cem\u003eEimeria\u003c/em\u003e in chickens in Guangdong province (n \u0026ndash; total number of samples; 95% CI: 95% confidence interval; OR: odds ratios; significant predictors in bold)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCategory (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eVery high pathogenicity (\u003cem\u003eE. necatrix\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eHigh pathogenicity (\u003cem\u003eE. tenella\u0026thinsp;+\u0026thinsp;E. brunetti\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMedium pathogenicity (\u003cem\u003eE. acervulina\u0026thinsp;+\u0026thinsp;E. maxima\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLow pathogenicity (\u003cem\u003eE.mitis\u0026thinsp;+\u0026thinsp;E. praecox\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStarter (n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.45 (79.49\u0026ndash;93.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.50 (5.76\u0026ndash;19.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.50 (27.64\u0026ndash;47.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.88 (36.71\u0026ndash;57.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.17 (19.91\u0026ndash;38.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrower (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.67 (78.83\u0026ndash;90.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.33 (16.49\u0026ndash;30.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.13 (1.04\u0026ndash;4.35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.67 (36.62\u0026ndash;52.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (0.80\u0026ndash;2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.33 (33.36\u0026ndash;49.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80 (0.48\u0026ndash;1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.33 (45.26\u0026ndash;61.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.78 (1.61\u0026ndash;4.79)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdult (n\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.86 (84.95\u0026ndash;94.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.68 (42.53\u0026ndash;58.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.19 (3.62\u0026ndash;14.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.27 (37.16\u0026ndash;53.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38 (0.82\u0026ndash;2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.92 (35.83\u0026ndash;52.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 (0.53\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.57 (34.51\u0026ndash;50.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.80 (1.04\u0026ndash;3.11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndigenous CBS (n\u0026thinsp;=\u0026thinsp;190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.79 (80.78\u0026ndash;90.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.47 (22.93\u0026ndash;36.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.21 (37.08\u0026ndash;51.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.26 (38.12\u0026ndash;52.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.79 (38.64\u0026ndash;52.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndigenous (n\u0026thinsp;=\u0026thinsp;204)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.24 (83.78\u0026ndash;92.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.35 (25.88\u0026ndash;38.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (0.75\u0026ndash;1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.16 (35.32\u0026ndash;48.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.62\u0026ndash;1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.16 (35.32\u0026ndash;48.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (0.59\u0026ndash;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.78 (34.37\u0026ndash;47.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.56\u0026ndash;1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlock size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;10000 (n\u0026thinsp;=\u0026thinsp;166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.14 (80.83\u0026ndash;91.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.51 (19.72\u0026ndash;33.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.77 (35.17\u0026ndash;50.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.0 (21.11\u0026ndash;68.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.17 (34.58\u0026ndash;49.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;10000 (n\u0026thinsp;=\u0026thinsp;228)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.72 (83.43\u0026ndash;92.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.21 (28.01\u0026ndash;40.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44 (0.93\u0026ndash;2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.42 (36.94\u0026ndash;49.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.69\u0026ndash;1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.33 (24.51\u0026ndash;62.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.74\u0026ndash;1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.30 (37.80-50.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 (0.73\u0026ndash;1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMulti-layer cage (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.0 (87.18\u0026ndash;100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.0 (35.65\u0026ndash;64.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.0 (25.94\u0026ndash;54.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.17 (34.58\u0026ndash;49.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.0 (33.66\u0026ndash;62.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGround floor (n\u0026thinsp;=\u0026thinsp;344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.05 (82.37\u0026ndash;89.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.20 (23.42\u0026ndash;32.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.39 (0.22\u0026ndash;0.72)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.60 (38.34\u0026ndash;48.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (0.63\u0026ndash;2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.74 (38.23\u0026ndash;51.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.45 (1.26\u0026ndash;4.76)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.73 (37.48\u0026ndash;47.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81 (0.45\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDrinking water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRunning water (n\u0026thinsp;=\u0026thinsp;139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.33 (80.55\u0026ndash;92.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.02 (15.94\u0026ndash;30.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.80 (43.39\u0026ndash;60.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.76 (38.36\u0026ndash;55.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.69 (28.58\u0026ndash;44.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroundwater (n\u0026thinsp;=\u0026thinsp;255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.45 (83.36\u0026ndash;91.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.29 (29.39\u0026ndash;41.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.82 (1.14\u0026ndash;2.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.43 (32.42\u0026ndash;44.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.58 (0.38\u0026ndash;0.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.96 (35.86\u0026ndash;48.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82 (0.54\u0026ndash;1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.06 (40.89\u0026ndash;53.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.53 (1.0-2.34)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVaccine (n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.11 (83.12\u0026ndash;100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.42 (5.51\u0026ndash;31.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.32 (62.15\u0026ndash;90.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.62 (2.06\u0026ndash;10.38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.0 (33.34\u0026ndash;66.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51 (0.74\u0026ndash;3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.21 (18.41\u0026ndash;50.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrug (n\u0026thinsp;=\u0026thinsp;188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.36 (84.91\u0026ndash;93.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.26 (22.69\u0026ndash;35.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83 (0.76\u0026ndash;4.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.30 (31.29\u0026ndash;45.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 (0.58\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.74 (38.56\u0026ndash;52.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (0.83\u0026ndash;1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.68 (37.51\u0026ndash;51.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55 (0.75\u0026ndash;3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCombined (n\u0026thinsp;=\u0026thinsp;168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.33 (77.64\u0026ndash;89.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.71 (28.39\u0026ndash;43.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.46 (1.02\u0026ndash;5.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.07 (33.56\u0026ndash;48.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.88 (32.40-47.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.05 (36.46\u0026ndash;51.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51 (0.73\u0026ndash;3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNecrotic enteritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;223)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.82 (74.51\u0026ndash;85.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.87 (17.31\u0026ndash;28.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.12 (31.69\u0026ndash;44.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.63 (27.38\u0026ndash;39.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.57 (32.13-45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.49 (93.71\u0026ndash;99.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.52 (34.06\u0026ndash;48.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.57 (1.05\u0026ndash;2.36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.71 (42.14\u0026ndash;57.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.61 (1.07\u0026ndash;2.40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.73 (49.22\u0026ndash;64.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.59 (1.72\u0026ndash;3.90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.71 (42.14\u0026ndash;57.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.57 (1.05\u0026ndash;2.36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern (n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.10 (87.67\u0026ndash;98.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.79 (6.40-21.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.72 (41.01\u0026ndash;62.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.47 (46.87\u0026ndash;68.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.12 (1.20\u0026ndash;3.74)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.68 (33.05\u0026ndash;54.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern (n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.0 (82.80\u0026ndash;97.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.29 (13.99\u0026ndash;34.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01 (0.88\u0026ndash;4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.14 (25.54\u0026ndash;48.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55 (0.29\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.57 (36.57\u0026ndash;60.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48 (0.81\u0026ndash;2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.57 (26.88\u0026ndash;50.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81 (0.43\u0026ndash;1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorthern (n\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.80 (84.61-97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.97 (37.07\u0026ndash;54.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.32 (2.63\u0026ndash;10.75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.52 (30.79\u0026ndash;48.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61 (0.35\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.48 (26.94\u0026ndash;44.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86 (0.51\u0026ndash;1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.74 (33.91\u0026ndash;51.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.55\u0026ndash;1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePearl River delta (n\u0026thinsp;=\u0026thinsp;113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.0 (73.52\u0026ndash;86.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.86 (23.14\u0026ndash;40.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.92 (1.41\u0026ndash;6.04)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.25 (34.95\u0026ndash;53.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74 (0.42\u0026ndash;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.94 (29.81\u0026ndash;48.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.90 (37.56\u0026ndash;56.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (0.65-2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariable logistic regression analysis of risk factors associated with prevalence of \u003cem\u003eEimeria\u003c/em\u003e in chickens in Guangdong province (n \u0026ndash; total number of samples; 95% CI: 95% confidence interval; OR: odds ratios; significant predictors in bold)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 10.1124%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 17.5443%;\"\u003e\n \u003cp\u003eVery high pathogenicity\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003eE. necatrix\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 17.5443%;\"\u003e\n \u003cp\u003eHigh pathogenicity\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003eE. tenella\u0026thinsp;+\u0026thinsp;E. brunetti\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 18.6409%;\"\u003e\n \u003cp\u003eMedium pathogenicity\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003eE. acervulina\u0026thinsp;+\u0026thinsp;E. maxima\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 18.0317%;\"\u003e\n \u003cp\u003eLow pathogenicity\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003eE.mitis\u0026thinsp;+\u0026thinsp;E. praecox\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.1124%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eStarter (n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.1124%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eGrower ((n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e2.12 (0.95\u0026ndash;4.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e1.50 (0.83\u0026ndash;2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003e0.72 (0.40\u0026ndash;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.90 (1.60\u0026ndash;5.26)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.1124%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eAdult (n\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.90 (4.91\u0026ndash;24.19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e1.37 (0.77\u0026ndash;2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003e0.84 (0.47\u0026ndash;1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.99 (1.11\u0026ndash;3.56)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.1124%;\"\u003e\n \u003cp\u003eFarming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eMulti-layer cage (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.1124%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eGround floor (n\u0026thinsp;=\u0026thinsp;344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.31 (0.12\u0026ndash;0.82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e0.96 (0.41\u0026ndash;2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003e2.20 (0.89\u0026ndash;5.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003e0.80 (0.34\u0026ndash;1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 10.1124%;\"\u003e\n \u003cp\u003eDrinking water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eRunning water (n\u0026thinsp;=\u0026thinsp;139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eGroundwater (n\u0026thinsp;=\u0026thinsp;255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e1.27 (0.61\u0026ndash;2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e0.64 (0.35\u0026ndash;1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003e1.11 (0.61\u0026ndash;2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003e0.89 (0.49\u0026ndash;1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.1124%;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eVaccine (n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.52 (1.21\u0026ndash;16.91)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.1124%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eDrug (n\u0026thinsp;=\u0026thinsp;188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e0.55 (0.12\u0026ndash;2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e0.92 (0.47\u0026ndash;1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003e1.37 (0.49\u0026ndash;3.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003e2.23 (0.79\u0026ndash;6.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.1124%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eCombined (n\u0026thinsp;=\u0026thinsp;168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e1.12 (0.20\u0026ndash;6.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003e1.26 (0.35\u0026ndash;4.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003e2.55 (0.72\u0026ndash;9.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 10.1124%;\"\u003e\n \u003cp\u003eNecrotic enteritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;223)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.22 (1.93\u0026ndash;5.37)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.82 (1.19\u0026ndash;2.78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.69 (1.76\u0026ndash;4.11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.57 (1.04\u0026ndash;2.38 )\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.1124%;\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eEastern (n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.1124%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eWestern ((n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e2.57 (0.71\u0026ndash;9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e0.94 (0.39\u0026ndash;2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003e0.59 (0.25\u0026ndash;1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003e0.56 (0.24\u0026ndash;1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.1124%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003eNorthern (n\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.14 (1.12\u0026ndash;15.32)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e1.36 (0.51\u0026ndash;3.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003e0.42 (0.16\u0026ndash;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003e0.65 (0.25\u0026ndash;1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.1124%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2548%;\"\u003e\n \u003cp\u003ePearl River delta (n\u0026thinsp;=\u0026thinsp;113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.29 (1.01\u0026ndash;10.79)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.0617%;\"\u003e\n \u003cp\u003e1.63 (0.71\u0026ndash;3.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.9146%;\"\u003e\n \u003cp\u003e0.43 (0.19-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6044%;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5491%;\"\u003e\n \u003cp\u003e0.81 (0.36\u0026ndash;1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.4826%;\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEimeria\u003c/strong\u003e \u003cstrong\u003especies profiles associated with necrotic enteritis occurrence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlations between the infection of \u003cem\u003eEimeria\u003c/em\u003e species and the occurrence of NE were shown in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. According to univariate analysis, \u003cem\u003eE. necatrix\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \u003cem\u003eE. tenella\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), \u003cem\u003eE. brunetti\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), \u003cem\u003eE. acervulina\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \u003cem\u003eE. maxima\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and \u003cem\u003eE. praecox\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) infection were associated with NE occurrence. Due to the observation of univariate analysis, \u003cem\u003eE. mitis\u003c/em\u003e infection was not considered to the multivariate analysis. Logistic regression revealed increased risk of NE in flocks was highly associated with the infection of \u003cem\u003eE. necatrix\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;2.20; 95% CI: 1.38\u0026ndash;3.49; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cem\u003eE. acervulina\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;2.37; 95% CI: 1.52\u0026ndash;3.71; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariable logistic regression analysis of risk factors associated with NE occurrence (95% CI: 95% confidence interval; OR: odds ratios; significant predictors in bold)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. tested\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria necatrix\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.76 (31.0-42.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.20 (49.32\u0026ndash;67.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.40 (1.55\u0026ndash;3.70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.20 (1.38\u0026ndash;3.49)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria tenella\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.98 (32.02\u0026ndash;43.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.68 (45.19\u0026ndash;62.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.89 (1.24\u0026ndash;2.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32 (0.83\u0026ndash;2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria brunetti\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.75 (34.33\u0026ndash;45.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.44 (47.18\u0026ndash;69.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.13 (1.29\u0026ndash;3.54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49 (0.85\u0026ndash;2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria acervulina\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.06 (29.12-41.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.04 (49.86\u0026ndash;66.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.56 (1.68\u0026ndash;3.91)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.37 (1.52\u0026ndash;3.71)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria maxima\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.68 (35.29\u0026ndash;46.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.56 (43.80-67.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.82 (1.09\u0026ndash;3.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (0.64\u0026ndash;2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria mitis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.41 (35.33\u0026ndash;47.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.10 (38.67\u0026ndash;55.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26 (0.83\u0026ndash;1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEimeria praecox\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.73 (35.39\u0026ndash;46.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.92 (44.56\u0026ndash;69.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.92 (1.12\u0026ndash;3.29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42 (0.79\u0026ndash;2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eCoccidiosis represents a significant economical challenge for the global poultry industry. This study investigated the prevalence of \u003cem\u003eEimeria\u003c/em\u003e species in Guangdong province, filling a critical research gap [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The overall prevalence of coccidiosis in Guangdong (87.06%; 343/394) was higher than that in Zhejiang province in China (30.7%; 95/310) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], Shandong province in China (65.8%; 50/76) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], Korean (75%; 291/388) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], Serbia (59%; 59/100) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], north India (28.5%; 171/600) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and southwestern Nigeria (41.3%; 2292/5544) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The heightened prevalence in Guangdong province could be attributed to the climatic conditions, characterized by increased temperature and humidity, which promote the propagation of \u003cem\u003eEimeria\u003c/em\u003e in broiler flocks. Our finding aligned more closely with previous reports from other tropical and subtropical regions and countries, including Anhui province in China (87.75%; 150/171) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], two northern Indian states (81.03%; 47/58) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and Greece (85.7%; 36/42) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Conversely, higher prevalence rates were documented in Henan province (96.70%; 176/182) and Hubei province in China (97.79%; 133/136) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Colombia (96.3%; 236/245) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], Australia (98%; 255/260) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Japan (91.9%; 33/37) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and northeastern Algeria (99.5%; 186/187) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This variability could be attributed to differing climate conditions, seasonal variations, different terrains and management practices in different regions and countries.\u003c/p\u003e \u003cp\u003eSeven distinct \u003cem\u003eEimeria\u003c/em\u003e species were identified within broiler chicken farms in Guangdong province, with the most prevalent species being \u003cem\u003eE. acervulina\u003c/em\u003e (36.29%), \u003cem\u003eE. mitis\u003c/em\u003e (35.03%), \u003cem\u003eE. tenella\u003c/em\u003e (34.52%), and \u003cem\u003eE. necatrix\u003c/em\u003e (30.96%). It is well-known that the interactions between \u003cem\u003eEimeria\u003c/em\u003e species and crowing effects play a pivotal role in oocyst production [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. \u003cem\u003eE. acervulina\u003c/em\u003e and \u003cem\u003eE. tenella\u003c/em\u003e exhibit higher productive potential, and in cases of mixed infection, \u003cem\u003eE. acervulina\u003c/em\u003e tends to suppress the oocyst production of \u003cem\u003eE. necatrix\u003c/em\u003e, \u003cem\u003eE. maxima\u003c/em\u003e and \u003cem\u003eE. brunetti\u003c/em\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our study found that single-species infections was predominant (42.86%), and only 39.94% of samples contained two to three \u003cem\u003eEimeria\u003c/em\u003e species within a single fecal sample. The most prevalent combinations were \u003cem\u003eE. acervulina\u003c/em\u003e\u0026ndash;\u003cem\u003eE. tenella\u003c/em\u003e (15.48%), followed by \u003cem\u003eE. acervulina\u003c/em\u003e\u0026ndash;\u003cem\u003eE. necatrix\u003c/em\u003e (14.21%), aligning with findings from various regions worldwide, including Colombia [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], Iran [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], India [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and Alegeria [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultivariable logistic regression analysis identified several potential risk factors associated with \u003cem\u003eEimeria\u003c/em\u003e species prevalence, with bird age emerging as the most important factor. Our results indicated that flocks with adult chickens faced the highest risk of \u003cem\u003eE. necatrix\u003c/em\u003e infection (OR\u0026thinsp;=\u0026thinsp;11.13, 95% CI: 4.98\u0026ndash;24.89; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding is consistent with the previous reports suggesting higher prevalence rates among adult birds compared to birds in other ages [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, it contrasts with studies by Lawal et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and Khursheed et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which reported that younger birds were more susceptible to infection than older birds. This discrepancy might be attributed to variations in the mainly prevalence of \u003cem\u003eEimeria\u003c/em\u003e species. \u003cem\u003eE. necatrix\u003c/em\u003e is known to possess lower reproductive capabilities and as a \u0026lsquo;poor competitor\u0026rsquo; compared to other species, hence being more commonly detected in older birds [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Notably, outbreaks due to \u003cem\u003eE. necatrix\u003c/em\u003e predominantly occur in older birds aged 9\u0026ndash;14 weeks [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The increase of epidemic \u003cem\u003eE. necatrix\u003c/em\u003e prevalence observed in this study underscores the importance of improving preventative measures.\u003c/p\u003e \u003cp\u003eIn the present study, \u003cem\u003eE. necatrix\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;2.20, 95% CI: 1.38\u0026ndash;3.49; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cem\u003eE. acervulina\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;2.37, 95% CI: 1.52\u0026ndash;3.71; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) infections were highly associated with NE occurrence. Similarly, previous study found that \u003cem\u003eEimeria\u003c/em\u003e species infection rates was significantly associated with the history of clostridiosis in farms (OR\u0026thinsp;=\u0026thinsp;2.6, 95% CI: 1.19\u0026ndash;2.78; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Previous studies have shown that \u003cem\u003eEimeria\u003c/em\u003e infection is a major predisposing factor for NE [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Coccidia-induced damage to the intestinal epithelium creates an environment allowing rapid replication and toxin production of \u003cem\u003eClostridium perfringens\u003c/em\u003e [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. As a result, \u003cem\u003eEimeria\u003c/em\u003e species, particularly \u003cem\u003eE. acervulina\u003c/em\u003e, \u003cem\u003eE. maxima\u003c/em\u003e and \u003cem\u003eE. necatrix\u003c/em\u003e, are often used experimentally in conjunction with \u003cem\u003eC. perfringens\u003c/em\u003e to induce NE. In this study, NE was significantly associated with both \u003cem\u003eE. necatrix\u003c/em\u003e and \u003cem\u003eE. acervulina\u003c/em\u003e. However, there is some debate over whether the more virulent \u003cem\u003eEimeria\u003c/em\u003e, such as \u003cem\u003eE. necatrix\u003c/em\u003e are superior to the less virulent ones like \u003cem\u003eE. acervulina\u003c/em\u003e in terms of predisposing bird to NE [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFactors including geographical locations, control methods used, and management type displayed association with a higher prevalence of coccidia, althoght these association were weaker. The association between geographical variation and elevated prevalence of coccidia has been reported in previous studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In this study, the risk factor of \u003cem\u003eE. necatrix\u003c/em\u003e occurrence was 4.14 times (95% CI: 1.12\u0026ndash;15.32; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and 3.29 times (95% CI: 1.01\u0026ndash;10.79; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) higher in northern Guangdong and Peal River delta, respectively, compare to eastern Guangdong. This might be attributed to heavier rainfall (approximately 149 mm/year) and relatively lower humidity (approximately 57\u0026ndash;64%) in both northern Guangdong and Peal River delta. These findings are consistent with those reported by Waldenstedt et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] that the sporulation of \u003cem\u003eE. maxima\u003c/em\u003e oocysts was poorest under the conditions of high moisture content of 62%, suggesting that oocyst sporulation may be more efficient in drier litter [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, a high risk of \u003cem\u003eE. tenella\u003c/em\u003e\u0026ndash;\u003cem\u003eE. brunetti\u003c/em\u003e infection in vaccinated flocks was identified compared to those administrated a combination of vaccines and anticoccidials (OR\u0026thinsp;=\u0026thinsp;4.52, 95% CI: 1.21\u0026ndash;16.91; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This study also found a high prevalence of \u003cem\u003eE. brunetti\u003c/em\u003e (19.54%) in Guangdong, compared with previous study (6.6%) in China [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], for which no commercial vaccines containing \u003cem\u003eE. brunetti\u003c/em\u003e available in China. Given its classification as a highly pathogenic species, the inclusion of \u003cem\u003eE. brunetti\u003c/em\u003e in vaccines in China may be warranted. Furthermore, previous studies found that chickens raised by free-range type of management had a higher coccidiosis occurrence compared to those raised by cages [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], since the transmission of sporulated oocysts of coccidia is mainly through the fecal-oral route. However, in this study, a high risk of \u003cem\u003eE. necatrix\u003c/em\u003e infection was also found in multi-layer caged flocks, highlighting the necessity of vaccination in coccidiosis prevention in modern poultry farm. Coccidia prevalence in birds raised in multi-layer cages is high may be attributed to high bird density and suboptimal cage design or maintenance. Further studies with larger sample size are warranted to explore the prevalence of \u003cem\u003eEimeria\u003c/em\u003e in flocks administrated different farming methods.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study underscores the substantial prevalence of \u003cem\u003eEimeria\u003c/em\u003e species infections in chickens across Guangdong province, China. The predominant species identified were \u003cem\u003eE. acervuline, E. mitis\u003c/em\u003e, \u003cem\u003eE. tenella\u003c/em\u003e, and \u003cem\u003eE. necatrix\u003c/em\u003e. The analysis of risk factors showed that NE occurrence, control methods, geographical variation, and bird age were associated with \u003cem\u003eEimeria\u003c/em\u003e species infection in chickens. In addition, the findings herein have shed the light on the necessity of considering \u003cem\u003eE. brunetti\u003c/em\u003e, a high pathogenetic strain, for inclusion in vaccines in China. In light of the identified risk factors, it is imperative to implement effective control strategies and management practices to reduce infections and subsequent economic losses in poultry farming.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy area and farms\u003c/h2\u003e \u003cp\u003eThe study was conducted across four distinct regions, geographically spanning between 20\u0026deg;09'\u0026ndash;25\u0026deg;31' north latitude and 109\u0026deg;45'\u0026ndash;117\u0026deg;20' east longitude located in south China, covering a total land area of 179,800 km\u003csup\u003e2\u003c/sup\u003e. The study was carried out over an extensive timeframe, spanning from April 2020 to November 2021. The climate in Guangdong is subtropical with mild winters and hot, muggy summers. The annual average temperature was about 23\u0026ndash;25\u0026deg;C. Furthermore, the annual averages relative humidity levels ranged from 57\u0026ndash;77%. The average monthly rainfall was approximately from 118 mm to 150 mm, drawing data from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldweatheronline.com/\u003c/span\u003e\u003cspan address=\"https://www.worldweatheronline.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e as the source (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Samples were collected from 89 broiler farms (21 from eastern Guangdong, 19 from western Guangdong, 18 from northern Guangdong, and 31 from Peal River delta) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Each farm had 2\u0026ndash;20 houses, accommodating bird populations ranging from 5000 to 40000 individuals at a density of 10\u0026ndash;16 birds/m\u003csup\u003e2\u003c/sup\u003e. The predominant broiler breeds are three-yellow chicken and spotted-brown chicken. The bedding materials in use were wood shavings or rice husk. A questionnaire was administered at each farm in parallel with fecal sample collection. The questionnaire included questions pertaining to key factors such as bird age, bird breed, flock size, farming method, source of drinking water, and control methods employed.\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\u003eManagement of characteristics of broiler farms in four regions of Guangdong, China during 2020 to 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorthern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeal River delta\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual average temperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual average humidity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual average rainfall (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e149.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenetic line in number of sampled farms\u003csup\u003ea\u003c/sup\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndigenous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\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\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndigenous CBs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\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\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLitter composition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewood shavings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewood shavings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewood shavings/\u003c/p\u003e \u003cp\u003erice husk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ewood shavings/\u003c/p\u003e \u003cp\u003erice husk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eground floor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eground floor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eground floor/\u003c/p\u003e \u003cp\u003emulti-layer cage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eground floor/\u003c/p\u003e \u003cp\u003emulti-layer cage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of drinking water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003erunning water/\u003c/p\u003e \u003cp\u003egroundwater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003erunning water/\u003c/p\u003e \u003cp\u003egroundwater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erunning water/\u003c/p\u003e \u003cp\u003egroundwater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003erunning water/\u003c/p\u003e \u003cp\u003egroundwater\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage age of birds at sampling (min. to max.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (20\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (23\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (22\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (17\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlock size (min. to max.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11667 (5000\u0026ndash;20000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14975 (9000\u0026ndash;20000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17341 (7000\u0026ndash;40000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11669 (8000\u0026ndash;23000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e 89 total surveys.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFecal sample collection and analysis\u003c/h3\u003e\n\u003cp\u003eFresh fecal samples were collected from various locations within each poultry house. Each sample weighed approximately 250 g consisted of 30 fresh fecal droppings gathered from a single house. A total of 394 fecal samples were collected from 89 farms and were placed in labelled zipped plastic bags. The samples were transported immediately to the laboratory and stored at 4\u0026deg;C until using. All the collected samples underwent microscopic examination for the presence of \u003cem\u003eEimeria\u003c/em\u003e oocysts, as previously described [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Moreover, PCR was employed to identify the specific \u003cem\u003eEimeria\u003c/em\u003e species within the fecal samples.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenomic DNA extraction\u003c/h2\u003e \u003cp\u003eBefore the DNA extraction, each sample was mixed with an equal volume of sterile ddH\u003csub\u003e2\u003c/sub\u003eO and was homogenized using a blender. Subsequently, aliquots of 200 \u0026micro;l of these prepared samples were transferred into a 1.5 ml Eppendorf tube for DNA extraction. Genomic DNA extraction was performed using the E.Z.N.A.\u0026reg; Stool DNA Kit according to the manufacturer\u0026rsquo;s protocol (Omega, D4015). The DNA was stored at -20\u0026deg;C until using.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMolecular characterization of\u003c/b\u003e \u003cb\u003eEimeria\u003c/b\u003e \u003cb\u003especies\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eEimeria\u003c/em\u003e species were identified by PCR using species-specific primers (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) as previously described by Schnitzler et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and Haug et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For each sample, a total volume of 20 \u0026micro;l was mixed, which includes 10 \u0026micro;l of Premix \u003cem\u003eTaq\u003c/em\u003e\u0026trade; (Takara, RR901A), 500 nm of species-specific for forward and reverse primers, 2 \u0026micro;l of DNA sample and 6 \u0026micro;l of ddH\u003csub\u003e2\u003c/sub\u003eO. The amplification was performed using T100\u0026trade; thermal cycler (Bio-Rad, USA) following cycling condition of an initial denaturation step at 95\u0026deg;C for 2 min, followed by 35 repeat cycles, each consisting of 30 seconds of denaturation at 95\u0026deg;C, 30 seconds of annealing at 62\u0026deg;C and a 1 minute of extension step at 72\u0026deg;C with a final extension continued for 3 minutes. The PCR products were determined by electrophoresis on 1.5% agarose gels.\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\u003ePCR primers for the seven chicken \u003cem\u003eEimeria\u003c/em\u003e species.\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\u003e\u003cem\u003eEimeria\u003c/em\u003e species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer sequences 5\u0026prime; \u0026ndash; 3\u0026prime;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnealing temperature (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpected amplicon size (bp)\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\u003eEimeria necatrix\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENF\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTACATCCCAATCTTTGAATCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e383\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\u003eENR\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGCATACTAGCTTCGAGCAAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEimeria tenella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eETF\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAATTTAGTCCATCGCAACCCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e271\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\u003eETR\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGAGCGCTCTGCATACGACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEimeria brunetti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEBF\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGATCAGTTTGAGCAAACCTTCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e310\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\u003eEBR\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGGTCTTCCGTACGTCGGAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEimeria acervulina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEAF\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGCTTGGATGATGTTTGCTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e321\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\u003eEAR\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGAACGCAATAACACACGCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEimeria maxima\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmuF\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTGGGACTGTGGTGATGGGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e162\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\u003eEmuR\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACCAGCATGCGCTCACAACCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEimeria mitis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEMIF\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTATTTCCTGTCGTCGTCTCGC\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\u003e306\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\u003eEMIR\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTATGCAAGAGAGAATCGGGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEimeria praecox\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEPF\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCATCATCGGAATGGCTTTTTGA\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\u003e368\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\u003eEPR\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAATAAATAGCGCAAAATTAAGCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e Primers described by Schnitzler et al. (1998)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eb\u003c/sup\u003e Primers described by Haug et al. (2007)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ec\u003c/sup\u003e Primers described by Schnitzler et al. (1999)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMolecular identification of\u003c/b\u003e \u003cb\u003eClostridium perfringen\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe identification of \u003cem\u003eClostridium perfringen\u003c/em\u003e in fecal samples was conducted using quantitative real-time PCR (qPCR) targeting the alpha toxin gene, as described by Mohiuddin et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The qPCR was carried out in a reaction mixture of 20 ul, containing TB Green \u003cem\u003ePremix Ex Taq\u003c/em\u003e II (Takara, RR820B) (10 \u0026micro;L), forward primers (1 \u0026micro;L), reverse primers (1 \u0026micro;L), template DNA 1\u0026micro;L (150\u0026ndash;200 ng), and ddH\u003csub\u003e2\u003c/sub\u003eO (7 \u0026micro;L). The amplification process was performed using CFX Connect\u0026trade; Real-Time PCR System (Bio-Rad, USA). The amplification program was at 95\u0026deg;C for 30 s, 35 cycles of denaturation at 95\u0026deg;C for 15 s, annealing at 60\u0026deg;C for 30 s, and a final step for dissociation at 95\u0026deg;C for 10 s, 65\u0026deg;C for 5 s, and 95\u0026deg;C for 5 s.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using software IBM SPSS Statistics 27.0 (SPSS Inc., \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.spss.com.hk\u003c/span\u003e\u003cspan address=\"http://www.spss.com.hk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Descriptive statistics including bird age, bird breed, flock size, farming type, type of drinking water, control strategy, and presence of NE were obtained from the questionnaires. Initially, the prevalence of \u003cem\u003eEimeria\u003c/em\u003e spp. infections with a 95% confidence interval (CI) was calculated. Then, the predictor variables associated with the presence of different type of \u003cem\u003eEimeria\u003c/em\u003e spp. were assessed using univariable and multivariable logistic regression models. Multivariable models were built by using forward stepwise logistic regression procedures (with inclusion if \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Chi-square test or Fisher\u0026rsquo;s exact test was used to compare the prevalence of one or more \u003cem\u003eEimeria\u003c/em\u003e infection in variables according to age, breed, flock size, farming type, drinking water source, control strategy, region and presence of NE and the odds ratios (OR, with 95% CI) were calculated to assess the associations between participants\u0026rsquo; characteristics and \u003cem\u003eEimeria\u003c/em\u003e species infection. Data with \u003cem\u003ep\u003c/em\u003e values\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered as statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cem\u003eE. necatrix \u0026nbsp; \u0026nbsp; \u0026nbsp; Eimeria necatrix\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eE. tenella \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Eimeria tenella\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eE. brunetti \u0026nbsp; \u0026nbsp; \u0026nbsp; Eimeria brunetti\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eE. acervulina \u0026nbsp; \u0026nbsp; Eimeria acervulina\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eE. maxima \u0026nbsp; \u0026nbsp; \u0026nbsp; Eimeria maxima\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eE. mitis \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Eimeria mitis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eE. praecox \u0026nbsp; \u0026nbsp; \u0026nbsp;Eimeria praecox\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;necrotic enteritis\u003c/p\u003e\n\u003cp\u003ePCR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eqPCR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Quantitative real-time polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eITS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;internal transcribed spacer\u003c/p\u003e\n\u003cp\u003eSCAR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;sequence characterized amplified region\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;odds ratios\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;confidence interval\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSL, NQ, and MS designed this study. SL, XL, QZ and ZY collected samples. CW, JL, ML, JH, HC, YS, and XC performed experiments. SL, XL, YZ, LY, JZ, NQ, and MS interpreted the results and drafted the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China (2023YFD1801202) , The open competition program of top ten critical priorities of Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province (2023SDZG02), Key Realm R\u0026amp;D Program of Guangdong Province (2023B0202150001), Opening Project of State Key Laboratory of Swine and Poultry Breeding Industry (2023QZ-NK05, 2022GZ07), Science and technology project of Yunfu (2022020202), Science and technology project of Guangzhou (2023B04J0137, 2023A04J0789), Special fund for scientific innovation strategy-construction of high level Academy of Agriculture Science (202110TD, 202122TD, R2020PY-JC001, R2019YJ-YB3010, R2020PY-JG013, R2020QD-048, R2021PY-QY007, R2023PY-JG018), The Project of Collaborative Innovation Center of GDAAS (XTXM202202).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll samples used in this study were fecal samples submitted by farm veterinarian and no human samples/materials are involved. The protocol of this study has been reviewed and approved by the Animal Care and Use Committee of the Institute of Animal Health, Guangdong Academy of Agricultural Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQingfeng Zhou and Zhuanqiang Yan are employed by Wen\u0026rsquo;s Foodstuffs Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChapman HD. Milestones in avian coccidiosis research: a review. Poult Sci. 2014; 93(3):501-11.\u003c/li\u003e\n\u003cli\u003eBlake DP, Knox J, Dehaeck B, Huntington B, Rathinam T, Ravipati V, et al. 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Microorganisms. 2023;11(3):622.\u003c/li\u003e\n\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":"Broiler, Eimeria spp., Prevalence, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-3890180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3890180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCoccidiosis is one of the most frequently reported disease in chickens, exerting a substantial economic impact on the poultry industry. This study aims to conduct an epidemiological investigation into the occurrence of \u003cem\u003eEimeria\u003c/em\u003e species and associated risk factors under intensive management conditions across four regions in Guangdong province, China.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 394 fecal samples were obtained from 89 broiler chicken farms, culminating in an overall positivity rate of 87.06%. The results showed that the identification of all seven \u003cem\u003eEimeria\u003c/em\u003e species, with \u003cem\u003eE. acervulina\u003c/em\u003e (36.29%), \u003cem\u003eE. mitis\u003c/em\u003e (35.03%), \u003cem\u003eE. tenella\u003c/em\u003e (34.52%) and \u003cem\u003eE. necatrix\u003c/em\u003e (30.96%) emerging as the most prevalent species. Remarkably, single-species infections were observed in 42.86% of instances, while two to three species mixed infections were detected in 39.94% of the samples. Moreover, brid age, farming practices, control strategies, farm locations, and the presence of necrotic enteritis (NE) proved significant risk factors. Notably, a strong correlation was observed between brid age, particularly in adult birds, and the occurrence of \u003cem\u003eE. necatrix\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A significant correlation was identified between the infection of \u003cem\u003eE. necatrix\u003c/em\u003e or \u003cem\u003eE. acervulina\u003c/em\u003e and the presence of NE in flocks (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Flocks from northern Guangdong and Peal River delta displayed higher prevalence of \u003cem\u003eE. necatrix\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Flocks under the control programs incorporating live vaccines correlated strongly with \u003cem\u003eE. tenella\u003c/em\u003e\u0026ndash;\u003cem\u003eE. brunetti\u003c/em\u003e infections (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMolecular analysis undertaken in this study, coupled with the correlation results, furnishes compelling evidence. Nevertheless, it is imperative to underscore the necessity for further surveys to delve deeper into the occurrence of different \u003cem\u003eEimeria\u003c/em\u003e species under intensive management conditions, which will contribute significantly to our knowledge of coccidia control in poultry.\u003c/p\u003e","manuscriptTitle":"Prevalence and geographic distribution of Eimeria species on commercial broiler farms in Guangdong, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 05:40:02","doi":"10.21203/rs.3.rs-3890180/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-01-24T09:54:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-23T10:10:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-23T10:10:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2024-01-23T06:48:15+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":"85773e95-169a-4be4-847f-95358956c594","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-07T20:06:18+00:00","versionOfRecord":{"articleIdentity":"rs-3890180","link":"https://doi.org/10.1186/s12917-024-03990-4","journal":{"identity":"bmc-veterinary-research","isVorOnly":false,"title":"BMC Veterinary Research"},"publishedOn":"2024-05-03 19:57:54","publishedOnDateReadable":"May 3rd, 2024"},"versionCreatedAt":"2024-01-25 05:40:02","video":"","vorDoi":"10.1186/s12917-024-03990-4","vorDoiUrl":"https://doi.org/10.1186/s12917-024-03990-4","workflowStages":[]},"version":"v1","identity":"rs-3890180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3890180","identity":"rs-3890180","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}