Prevalence data on chicken diseases in low-resource settings.
Systematic Review
Prevalence data on chicken diseases in low-resource settings.
The presence of transmissible disease in livestock has a major impact on welfare and economics in animal and public health. A lack of data enables the spread of diseases due to misinformed decision-making on prevention and control. Low-resource settings face challenges in providing data, turning data availability into a development issue. For this study, a large dataset (n = 997) was collected on prevalence and seroprevalence estimates on viral (n = 224), bacterial (n = 83) and parasitic (n = 690) diseases in backyard chickens in low- and middle-income countries (LMICs). These estimates originate from 306 studies identified during the screening phase of a systematic literature review. An attempt was made to classify the studies according to the Food and Agriculture Organization of the United Nations’classification system for family poultry production systems. Of the studies, 98.7% (302/306) focused on a single poultry production system, while 1.3% (4/306) targeted two different production systems. Within the group of studies that covered one production system, 85.4% (258/302) were classified as ‘small extensive scavenging or extensive scavenging,’‘small extensive scavenging’and/or ‘extensive scavenging’. In addition, 52% (159/306) of the studies did not report information on chicken breed type. No data were found on any relevant disease for 56.9% (78/137) of LMICs, signifying a potential data gap. Of the estimates on viral and bacterial diseases, 71.0% (218/307) corresponded to diseases notifiable to the World Organisation for Animal Health, highlighting a tendency to measure disease occurrence for diseases relevant to trade. The latter might not necessarily be priority diseases for the producers, however. Furthermore, 72.3% (222/307) of the estimates originate from random samples and could be used to estimate prevalence in backyard chickens using imputation methods, thus bridging the data gap.
