Quantification of risk factors of coccidiosis in broilers by using logistic regression analysis

被引:3
|
作者
Akcay, Aytac [1 ]
Ertugrul, Okan [2 ]
Gurcan, I. Safa [1 ]
Karaer, Zafer [3 ]
机构
[1] Ankara Univ, Fac Vet Med, Dept Biostat, TR-06100 Ankara, Turkey
[2] Ankara Univ, Fac Vet Med, Dept Genet, TR-06100 Ankara, Turkey
[3] Ankara Univ, Fac Vet Med, Dept Parasitol, TR-06100 Ankara, Turkey
来源
关键词
Broiler; coccidiosis; logistic regression analysis; odds ratio; risk factors; SALMONELLA-ENTERITIDIS INFECTION; EIMERIA-TENELLA;
D O I
10.1501/Vetfak_0000002474
中图分类号
S85 [动物医学(兽医学)];
学科分类号
0906 ;
摘要
The aim of this research was to determine the most efficient risk factors on broiler coccidiosis in Turkey. The study was performed in 1110 broiler chickens housed in 817 farms located in six geographical region of Turkey between September 2006 and September 2007. Survey questionnaires were held and faecal samples were collected from broiler flocks. Survey results were combined with laboratory findings. A logistic regression analysis was used to assess variables that influenced the occurrence of Coccidiosis. Firstly, simple logistic regression was performed for each variable by using presence or absence criteria of coccidiosis. Then, variables that were associated with coccidiosis-positive flocks at P value of <= 0.25 were included in multivariable logistic regression. In the present study, clinical or subclinical coccidiosis ratio was determined to be 56.2% in the analysis of the faeces samples. The multivariate logistic regression model for coceidiosis was completed in 10th step by using the backward elimination procedure. Overall classification ratio of final model was determined to be 87.3%. The results showed an enhanced risk of coccidiosis due to environmental and management factors such as season, number of chick house, age of chick, type of ventilation system, roof isolation, litter materials, having a type of farmyard which is easy to clean, time between production periods, leaving litter material to a safe distance after production period, presence of vermin, climate regulation and other diseases which might facilitate introduction of the parasite.
引用
收藏
页码:195 / 202
页数:8
相关论文
共 50 条
  • [21] Influence of Logistic Regression Models For Prediction and Analysis of Diabetes Risk Factors
    Maulana, Yufri Isnaini Rochmat
    Badriyah, Tessy
    Syarif, Iwan
    EMITTER-INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY, 2018, 6 (01) : 151 - 167
  • [22] Analysis of Risk Factors Affecting the Severity of Intersection Crashes by Logistic Regression
    Chen, Huiqin
    Cao, Libo
    Logan, David B.
    TRAFFIC INJURY PREVENTION, 2012, 13 (03) : 300 - 307
  • [23] Pelvic relaxation and associated risk factors:: The results of logistic regression analysis
    Gürel, H
    Gürel, SA
    ACTA OBSTETRICIA ET GYNECOLOGICA SCANDINAVICA, 1999, 78 (04) : 290 - 293
  • [24] Logistic regression analysis of risk factors for postoperative recurrence of spinal tumors and analysis of prognostic factors
    Zhang, Shanyong
    Yang, Lili
    Peng, Chuangang
    Wu, Minfei
    ONCOLOGY LETTERS, 2018, 15 (02) : 1716 - 1722
  • [25] Quantification of the Ecological Value of Railroad Development Areas Using Logistic Regression Analysis
    Kim, Min-Kyeong
    Park, Duckshin
    Kim, Dong Yeob
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 18 (22)
  • [26] Quantification of risk factors for mastitis, ovarian cyst, milk fever and ketosis (in Holstein cattle) using logistic regression in Holstein cattle
    Uribe, HA
    ARCHIVOS DE MEDICINA VETERINARIA, 1998, 30 (02) : 177 - 190
  • [27] Retrospective multivariate analysis of risk factors of breast cancer in a high-risk population using a logistic regression model
    Lumachi, F.
    Santeufemia, D. A.
    Tumolo, S.
    Lo Re, G.
    Pasqual, E. M.
    Chiara, G. B.
    Basso, S. M. M.
    EUROPEAN JOURNAL OF CANCER, 2013, 49 : S442 - S443
  • [28] Empirical factors of farmland transfer behavior using logistic regression analysis
    Wang Y.
    Wang, Yu, 2016, UK Simulation Society, Clifton Lane, Nottingham, NG11 8NS, United Kingdom (17): : 11.1 - 11.6
  • [29] Knowledge Estimation with HPV and Cervical Cancer Risk Factors Using Logistic Regression
    Omone, Ogbolu Melvin
    Gbenimachor, Alex Ugochukwu
    Kovacs, Levente
    Kozlovszky, Miklos
    IEEE 15TH INTERNATIONAL SYMPOSIUM ON APPLIED COMPUTATIONAL INTELLIGENCE AND INFORMATICS (SACI 2021), 2021, : 381 - 386
  • [30] Identification of Secondary Crash Risk Factors using Penalized Logistic Regression Model
    Kitali, Angela E.
    Alluri, Priyanka
    Sando, Thobias
    Wu, Wensong
    TRANSPORTATION RESEARCH RECORD, 2019, 2673 (11) : 901 - 914