Risk factors for infection with highly pathogenic influenza A virus (H5N1) in commercial chickens in Bangladesh

Abstract

A matched case-control study was carried out to identify risk factors for highly pathogenic avian influenza A virus (subtype H5N1) infection in commercial chickens in Bangladesh. A total of 33 commercial farms diagnosed with H5N1 before September 9, 2007, were enrolled as cases, and 99 geographically matched unaffected farms were enrolled as control farms. Farm data were collected using a pretested questionnaire, and analysed by matched-pair analysis and multivariate conditional logistic regression. Two factors independently and positively associated with H5N1 infection remained in the final model. They were 'farm accessible to feral and wild animals' (odds ratio [OR] 5.71, 95 per cent confidence interval [CI] 1.81 to 18.0, P=0.003) and 'footbath at entry to farm/shed' (OR 4.93, 95 per cent CI 1.61 to 15.1, P=0.005). The use of a designated vehicle for sending eggs to a vendor or market appeared to be a protective factor (OR 0.14, 95 per cent CI 0.02 to 0.88, P=0.036).

A Case-Control Study to Identify Risk Factors Associated with Avian Influenza Subtype H9N2 on Commercial Poultry Farms in Pakistan

“…Ponds, canals or water reservoirs can play an important role in the introduction of AI because these attract wild birds [16, 23], which may then contaminate the environment [17, 28]. Further wild birds may directly contaminate poultry sheds if they have access, acting as mechanical and/or biological vectors (shedding the virus in droppings) and are important particularly for introducing virus to new areas [23, 28].…”

“…Many published articles have quantified different risk factors for various subtypes of avian influenza in commercial poultry farms all over the world [10–23]. Previously published studies have demonstrated that avian influenza introduction, transmission and persistence are associated with poultry trading pattern [12, 20], human and poultry densities [18, 23], movement of human and fomites [10, 12, 14], low biosecurity [10, 13, 21, 22], proximity to water bodies [15, 16, 17, 23], distance from other commercial poultry farms [11, 13, 21], and proximity to roads [15, 16, 19]. These risk factors help to further identify high-risk farms/systems, which could be targeted for interventions such as vaccination or culling.…”

Interventions for avian influenza A (H5N1) risk management in live bird market networks

“…Here, poultry traders can mix and potentially transfer the virus either by trading infected poultry or by sharing contaminated equipment. In the absence of effective disinfection, traders may then act as a major source of exposure to infection for farms (13)(14)(15)(16)(17)(18). Once contaminated, some LBMs may even act as viral reservoirs, depending on the poultry management practices of their traders (19,20).…”

Preventive Veterinary Medicine

“…More HPAI was reported in the cool (average daily temperature of 15-20 • C) and dry winter months (December-March), while no disease was reported during the hot (average daily temperature of 30 • C) and wet summer season [June-August (Islam, 2009)]. It has been suggested that the H5N1 virus was introduced in Bangladesh by migratory birds (Biswas et al, 2009). Analyses of molecular DNA sequence data from 27 virus isolates collected in early 2007 (n = 10), mid 2008 (n = 10) and early 2009 (n = 7) showed that all virus isolates belonged to sub-Clade 2.2 with little difference (<5%) in genetic makeup (Genbank, 2002).…”

“…In Bangladesh, agricultural and ecological conditions of HPAI H5N1 spread and persistence may somewhat differ from those reported in Thailand and Vietnam as agriculture in Bangladesh is primarily extensive and subsistence, with much lower levels of long-distance trading (Dolberg, 2009). Although two epidemiological studies (Biswas et al, 2008(Biswas et al, , 2009) identified farm-level risk factors, spatial risk factors associated with the HPAI H5N1 spread at the national level is unknown. Additionally, epidemiological research needs to include the cluster analysis of disease occurrence and risk factors in specific locations and during specific time periods, which leads to define location-sensitive "hot spots" and timesensitive "hot times" risk maps (Gilbert et al, 2008).…”