Shama Ranjan Barua
Upazila Livestock Officer Rajasthali, Rangamati, Bangladesh
Tofazzal Md. Rakib
Department of Pathology and Parasitology, Faculty of Veterinary Medicine, Chittagong Veterinary and Animal Sciences University, Khulshi, Chittagong – 4225, Bangladesh
Mohammad Mahbubur Rahman
Department of Pathology and Parasitology, Faculty of Veterinary Medicine, Chittagong Veterinary and Animal Sciences University, Khulshi, Chittagong – 4225, Bangladesh
Sarina Selleck
University of Vermont, Burlington, VT 05405, USA
Md. Masuduzzaman
Department of Pathology and Parasitology, Faculty of Veterinary Medicine, Chittagong Veterinary and Animal Sciences University, Khulshi, Chittagong – 4225, Bangladesh
AMAM Zonaed Siddiki
Department of Pathology and Parasitology, Faculty of Veterinary Medicine, Chittagong Veterinary and Animal Sciences University, Khulshi, Chittagong – 4225, Bangladesh
Mohammad Alamgir Hossain
Department of Pathology and Parasitology, Faculty of Veterinary Medicine, Chittagong Veterinary and Animal Sciences University, Khulshi, Chittagong – 4225, Bangladesh
Sharmin Chowdhury
Department of Pathology and Parasitology, Faculty of Veterinary Medicine, Chittagong Veterinary and Animal Sciences University, Khulshi, Chittagong – 4225, Bangladesh
Calves, Prevalence, Risk factors, Rotavirus
the south-eastern part of Bangladesh under the Chittagong division, Bangladesh
Risk Management in Agriculture
Study area and period:
This study was conducted in three different distinct geographic areas situated in the south-eastern part of Bangladesh under the Chittagong division; hilly, coast and plain land. Administratively Bangladesh is divided into divisions (largest administrative area). Divisions are further divided into districts and districts into sub-districts. Chittagong metropolitan and Patiya Upazila (sub-district) were selected as plain land under Chittagong district, Rangamati Sadar and Kowkhali Upazila were chosen as the hilly area under Rangamati district and Chakaria and Pakua Upazila as the coastal area under the district of Cox?s Bazar using probability sampling. The present study was conducted from July 2015 through May 2016.
Sampling strategy and study population:
We followed a multi-stage (three stages) random sampling strategy to select study subjects. Our study area was Chittagong division and study unit was an individual calf. A list of all districts and sub-districts under Chittagong division was collected from Bangladesh Bureau of Statistics. Districts under the division were divided into 3 regions; hilly, coast and plain. At first stage of sampling, one district from each geographical region was selected using simple random sampling; Chittagong district was selected purposively though. At second stage, two sub-districts from each selected district were selected using random sampling. At the third stage, to select farms under sub-districts, we collected the sampling frame created by the Sub-project Manager Team under the project HEQEP CP: 3220, Chittagong Veterinary and Animal Sciences University (CVASU), Chittagong, Bangladesh for the dairy farms within Chittagong metropolitan area and Patiya. For other sub-districts no sampling frame was available and convenience sampling strategy was followed to select farms. However, during convenience sampling, discussions about the locations and variety of farms were discussed thoroughly with the local practitioners to include as much as variation possible in to the study from those areas. Where the sampling frame existed, a random number table was used to draw farms from the sampling frame. After the selection of dairy farms, all calves less than 6 weeks of age within the selected farms were picked for sample collection. Our targeted sample size was 395 calves. The sample size was calculated considering 50% estimated prevalence (due to unavailability of reliable estimate at the study area) and 5% allowable error. We selected 101 farms from Chittagong district, 65 farms from Cox?s Bazar district and 43 farms from Rangamati district. After completion of sampling procedure, we ended up with 411 samples from diarrheic and non-diarrheic calves within the mentioned age group.
Study design and data collection tool:
We followed a cross sectional survey design to collect data from the selected farms and calves. A questionnaire (for data collection) was developed before the initiation of the survey. A comprehensive literature review was done before formulating the questionnaire to congregate information about potential risk factors for rotaviral infection in calves. The draft questionnaire was discussed with selected practitioners working on calf diarrhea and to epidemiologist experienced with risk factor analysis. The questionnaire was corrected according to the suggestions of the experts. A pilot study was done including a small number of farms around the center of the research station (CVASU, Bangladesh) and was amended accordingly where necessary.
Specimen collection:
Fecal samples were collected from rectum using a disposable latex glove and in some cases the middle layer of a fresh voiding on the floor was collected when rectum was empty. All standard precautionary measures were taken in consideration during sample collection. All the samples were collected separately in a sterilized plastic container and labeled properly with the identification number, type and age of animal, location and time of sample collection etc. and transported carefully in an ice box to the laboratory preferably within 24 hours of collection.
Sample processing:
Laboratory analysis was done at the clinical pathology laboratory, Department of Pathology and Parasitology, CVASU, Chittagong on the day of collection. Fecal samples were divided into 2 ml aliquots and refrigerated. One aliquot was stored at 4°C until processing for the commercial ELISA test kits at the laboratory. The remaining 2 ml aliquots of feces were stored at -70°C for further testing, if required.
ELISA testing:
A total of 411 fecal samples were tested using a commercial ELISA kit for rotavirus (Antigenic ELISA kit for detection of rotavirus, Bio-X® Diagnostics; Jemelle, Belgium) according to the instruction of the manufacturer. Briefly, 100 μl of diluted samples (50 μl of dilution buffer and then 50 μl of undiluted feces) were plated into the wells of a microplate coated with the appropriate antibody. 2.7. Data management
The dependent variable was the calf-level rotavirus test result in ELISA (positive, negative).We started with 39 variables from the questionnaire based on preceding understanding hypothesized to be linked with rotaviral infection in calves in different countries. A farm level variable was defined as a variable with homogenous value for all calves in a farm across the study period. The variable “season” was extracted from the sample collection date. In Bangladesh, though officially there are six seasons, due to minimal variation in temperature and other environmental factors across different seasons, seasons can be divided broadly into 2 main types. For the present study we divided two seasons as: (1) winter = October, November, December, January, February, (2) summer = March, April, May, June, July, August, September. 2
Statistical analysis:
Throughout the study period, 411 calf test results from 210 dairy farms were incorporated into the analysis. Spearman correlation coefficient was used to assess correlation between the 39 independent variables incorporated in to the study. Variables with a Spearman correlation coefficient above 0.4 were judged correlated. Continuous variables were plotted using scatter plots for visual evaluation of correlation. During the selection procedure, depending on existing knowledge, biological plausibility of correlation between variables was also judged. Finally, univariable analysis was performed for the chosen explanatory variables and those having P-value ≤0.1 were selected for multivariable analysis. Descriptive analysis was accomplished by modes of frequency (N, %) of positive and negative calf level test results overall and stratified by selected explanatory variables.
Asian J. Med. Biol. Res. 2019, 5 (2), 107-116; doi: 10.3329/ajmbr.v5i2.42492; ISSN 2411-4472 (Print) 2412-5571 (Online)
Journal