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surveys and understanding of diagnostic test perfromance in local cattle populations. The aims of this paper were to determine the Fasciola species infecting cattle, develop a species specific serum antibody ELISA, assess the performance of the ELISA and use it to assess the prevalence of F. gigantica exposure in two important cattle-rearing areas of Cameroon.
A random sample of Fasciola parasites were collected and were all identified as F. gigantica (100%, CI:94.0-100%, n=60) using RAPD-PCR analysis. A F. gigantica antibody ELISA was developed and initially a diagnostic cut-off was determined using a sample of known positive and negative cattle. The initial cut-off was used as starting point to estimate an optimal cut-off to estimate the best combination of sensitivity and specificity. This was achieved through sampling a naturally infected population with known infection status (cattle slaughtered at Bamenda abattoir, North West Region (n=1112) and Ngaoundere abattoir, Vina Division, Adamawa Region (n=776) in Cameroon). These cattle were tested and results analysed using a Bayesian non-gold standard method. The optimal cut-off was 23.5, which gave a sensitivity of 65.3% and a specificity of 65.2%. The prevalence of exposure to F. gigantica was higher in cattle in Ngaoundere (56.4% CI: 50.2-60.0%) than Bamenda (0.6% CI: 0.0-1.4%).
Fasciola gigantica was identified as the predominant Fasciola species in Cameroon. Although the sensitivity and specificity F. gigantica antibody ELISA requires improvement, the test has shown to be a potentially useful tool in epidemiological studies. Highlighting the need for better understanding of the impact of F. gigantica infections on cattle production in Cameroon to improve cattle production in the pastoral systems of Central-West Africa. This paper also highlights that non-gold standard latent class methods are useful for assessing diagnostic test performance in naturally-infected animal populations in resource limited settings.
- Fasciola gigantica
- Diagnostic test performance
- Latent class methods
- Bayesian non-gold standard methods
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- 1 Finished
1/11/12 → 30/04/14