Factors contributing to AMR occurrence in infectious agents of broiler chickens in selected districts in Zambia.

Antimicrobial resistance (AMR) has remained one of the growing public health problems globally. The misuse and abuse of antimicrobials in agriculture, veterinary, and human medicine have been described as major contributing factors for the emergence of AMR. Globally, it is estimated that antimicrobial resistance (AMR) will be responsible for 10 million deaths per year by 2050 if steps are not taken immediately to combat and prevent
the spread of AMR. The aim of this study was to determine factors associated with AMR occurrence in broiler chickens in selected districts in Zambia, specifically to determine prevalence of AMR in broiler poultry from secondary data and also to analyse the factors contributing to AMR occurrence of broiler chickens in selected districts in Zambia. The study design was cross-sectional conducted in Ndola, Kitwe, Choma, Chilanga and Kafue districts. Thestudy population included small, medium and commercial scale broiler farmers. Both probability and non-probability sampling techniques were
employed and structured questionnaires were administered for collecting the information about the risk factors of AMR of poultry farmers. A total of 109broilers chicken’s farms were included in this study and overall AMR prevalence was 84.4% (92). In univariate analysis the variables age category, gender, marital status, sources of drinking water, electricity, poultry housing type, floor housing type, presence of drainage a system, destination of the drainage, bird population, poultry house fenced, use of protective gears, buying drugs from veterinary personnel, drug prescription when buying, drugs administered by veterinary personnel, drugs administered by an untrained farmer and administered by owner which had P< 0.25 were taken for further screening in the logistic model, The multivariate logistic regression model showed that gender, marital status, administration of drugs by veterinary personnel or the farmer, and the drainage destination were predictors of a broiler chicken farm having an antimicrobials resistance problem. The Pearson Chi-square goodness-of-fit test (p = 0.486) showed that the model fitted the data, thus increasing its reliability in predicting AMR farm status. The ReceiverOperating characteristic Curve (ROC) analysis demonstrated that the model was good in prediction farm-AMR status (ROC=0.8). The model had relatively high sensitivity and specificity in classifying the farm as having an AMR problem or not The high prevalence of AMR in broiler chicken suggests a greater antimicrobials use in poultry production in Zambia. Several potential risk factors for AMR identified in this study such as gender, marital status, administration of drugs by veterinary personnel or trained farmer, and the drainage destination.

Key words: Antimicrobial Resistance (AMR), Prevalence, Broiler, Poultry