Volume : 8, Issue : 1 , Article : 1


Report of the burden of bovine anaplasmosis is common in different areas of Bangladesh, including Sylhet district. However, epidemiological parameters of this disease are not well understood. This study aimed to conduct a cross-sectional study to investigate the prevalence and determinants of bovine anaplasmosis. The present study investigated a total of randomly selected 409 cattle of 64 farms from 12 subdistricts (upzilla) of Sylhet District. Microscopic examination of Giemsa’s stained thin blood smears was used to identify anaplasma infected cattle. Information on determinants was collected from farm record books and by interviewing farm owners. Prevalence was calculated as a proportion of infected cattle in a total number of cattle tested. A multivariable logistic regression model was used determine the association of hypothesized determinants with positivity for bovine anaplasmosis. Microscopic examination of thin blood smear identified two species of Anaplasma - namely, Anaplasma marginale, and Anaplasma centrale. The overall prevalence of bovine anaplasmosis was 22.74% (95% CI: 18.66 - 26.82). Prevalence of Anaplasma marginale and Anaplasma centrale were 12.71%; 95% (95% CI: 9.47 -15.96) and 6.60% (95% CI: 1.23 - 4.18) respectively. Rest, 3.42% (95% CI: 1.65%-5.19%) of the cattle found coinfected with Anaplasma marginale and Anaplasma centrale. Though age, breed, sex and tick infestation were considered plausible, only breed (p=0.02) was significantly associated with positivity for bovine anaplasmosis. Odd of overall bovine anaplasmosis was higher in local indigenous cattle than crossbred (OR = 1.98; 95% CI: 1.09 - 3.61). Study results indicate that burden of bovine anaplasmosis is apparently high in Sylhet district. A further investigation with molecular and serological techniques will provide a clearer scenario of disease burden. Besides identification of the carriers and vectors of bovine anaplasmosis is also necessary.

Keywords : Bovine anaplasmosis, Thin smear, Prevalence, Logistic regression, Determinants