We conducted a cross-sectional study in live bird markets (LBMs) in Dhaka and Chittagong, Bangladesh, to estimate the prevalence of avian influenza A(H5) and A(H9) viruses in different types of poultry and environmental areas by using Bayesian hierarchical logistic regression models. We detected these viruses in nearly all LBMs. Prevalence of A(H5) virus was higher in waterfowl than in chickens, whereas prevalence of A(H9) virus was higher in chickens than in waterfowl and, among chicken types, in industrial broilers than in cross-breeds and indigenous breeds. LBMs with >1 wholesaler were more frequently contaminated by A(H5) virus than retail-only LBMs. Prevalence of A(H9) virus in poultry and level of environmental contamination were also higher in LBMs with >1 wholesaler. We found a high level of circulation of both avian influenza viruses in surveyed LBMs. Prevalence was influenced by type of poultry, environmental site, and trading.

Aim:The study was conducted to detection and determination of concentration or level of antibiotic residues in milk and egg of local and commercial farms at Chittagong during December 2011 to June 2012.Materials and Methods:A total of 400 (200 milk and 200 egg) samples were collected from local and commercial dairy cows and local scavenging and commercial poultry farms, respectively. Microbial inhibition test and thin layer chromatography were used for screening and ultra-high performance liquid chromatography was used to estimate the concentrations of antibiotic residues in samples.Results:Tetracycline, amoxicillin, and ciprofloxacin residues were significantly (p ≤ 0.05) higher in commercial farms than local. The boiling insignificantly (p>0.05) reduced residues level in milk and egg. The average concentrations of amoxicillin residue in local milk, commercial milk, local egg, and commercial egg were 9.84 µg/ml, 56.16 µg/ml, 10.46 µg/g and 48.82 µg/g, respectively, in raw samples and were reduced to 9.81 µg/ml, 55.54 µg/ml, 10.29 µg/g, and 48.38 µg/g, respectively, after boiling.Conclusions:Proper maintaining of the withdrawal period and development of active surveillance system are highly recommended for public health security.

In order to control Highly Pathogenic Avian Influenza (HPAI) H5N1 and Low Pathogenic Avian Influenza (LPAI) H9N2 virus spread in endemically infected countries, a detailed understanding of infection patterns is required. We conducted cross-sectional studies in Bangladesh in 2016 and 2017, on 144 backyard, 106 broiler and 113 layer chicken farms. Although all sampled birds were negative for H5 virus by RT-PCR, H5 antibodies were detected in unvaccinated birds on all three farming systems. Higher H5 antibody prevalence was observed in ducks raised on backyard farms, 14.2% (95% CI: 10.0%-19.8%), compared to in-contact backyard chickens, 4.2% (95% CI: 2.8%-6.1%). The H5 antibody prevalence was lower in broiler chickens, 1.5% (95% CI: 0.9%-2.5%), compared to layer chickens, 7.8% (95% CI: 6.1%-9.8%). H9 viruses were detected by RT-PCR in 0.5% (95% CI: 0.2%-1.3%) and 0.6% (95% CI: 0.3%-1.5%) of broilers and layers, respectively, and in 0.2% (95% CI: 0.0%-1.2%) of backyard chickens. Backyard chickens and ducks showed similar H9 antibody prevalence, 16.0% (95% CI: 13.2%-19.2%) and 15.7% (95% CI: 11.3%-21.4%), which was higher compared to layers, 5.8% (95% CI: 4.3%-7.6%), and broilers, 1.5% (95% CI: 0.9%-2.5%). Over the course of a production cycle, H5 and H9 antibody prevalence increased with the age of backyard and layer chickens. Usually, multiple ducks within a flock were H5 antibody positive, in contrast to backyard chickens, broilers and layers where only individual birds within flocks developed H5 antibodies. Our findings highlight low virus circulation in healthy chickens of all production systems in Bangladesh, which is in contrast to high virus circulation reported from live bird markets. Data generated in this project can be used to adopt risk-based surveillance approaches in different chicken production systems in Bangladesh and to inform mathematical models exploring HPAI infection dynamics in poultry from the source of production.

Aquaculture systems are highly complex, dynamic and interconnected systems influenced by environmental, biological, cultural, socio-economic and human behavioural factors. Intensification of aquaculture production is likely to drive indiscriminate use of antibiotics to treat or prevent disease and increase productivity, often to compensate for management and husbandry deficiencies. Surveillance or monitoring of antibiotic usage (ABU) and antibiotic resistance (ABR) is often lacking or absent. Consequently, there are knowledge gaps for the risk of ABR emergence and human exposure to ABR in these systems and the wider environment. The aim of this study was to use a systems-thinking approach to map two aquaculture systems in Vietnam – striped catfish and white-leg shrimp – to identify hotspots for emergence and selection of resistance, and human exposure to antibiotics and antibiotic-resistant bacteria. System mapping was conducted by stakeholders at an interdisciplinary workshop in Hanoi, Vietnam during January 2018, and the maps generated were refined until consensus. Thereafter, literature was reviewed to complement and cross-reference information and to validate the final maps. The maps and component interactions with the environment revealed the grow-out phase, where juveniles are cultured to harvest size, to be a key hotspot for emergence of ABR in both systems due to direct and indirect ABU, exposure to water contaminated with antibiotics and antibiotic-resistant bacteria, and duration of this stage. The pathways for human exposure to antibiotics and ABR were characterised as: occupational (on-farm and at different handling points along the value chain), through consumption (bacterial contamination and residues) and by environmental routes. By using systems thinking and mapping by stakeholders to identify hotspots we demonstrate the applicability of an integrated, interdisciplinary approach to characterising ABU in aquaculture. This work provides a foundation to quantify risks at different points, understand interactions between components, and identify stakeholders who can lead and implement change.

No abstract