Comparative Evaluation of Diagnostic Tests for Brucellosis in Humans and Animals: A Meta-Analytical Approach

Brucellosis is a highly contagious, neglected, and re-emerging zoonotic bacterial disease that poses significant health and economic challenges globally for both humans and animals. Extensive literature is available for various diagnostic strategies: however, no comprehensive meta-analysis comparing the diagnostic tests used has been published. The present study aimed to estimate the relative risk (RR) of diagnostic tests used in humans and animals published between 2013 and 2023. Four databases were systematically searched, and the articles were screened using predefined inclusion and exclusion criteria. Ultimately, the screening process resulted in a total of 135 studies, including 328 comparisons of relevant data of 19,921 humans and 64,145 animals. The data from these studies were extracted, and the subgroup meta-analyses were conducted using the METABIN procedure in the &ldquo:meta&rdquo: package of the R statistical software (version 4.4.1). The forest plots were generated to estimate RR, and the funnel plots were used to assess publication and report bias. The subgroup analysis revealed that primary binding assays had higher comparative detection rates than the Rose Bengal plate test (RBPT) for brucellosis in humans [RR = 1.75 (95% CI: 1.35&ndash:2.26), I2 = 73%]. Slow agglutination tests had lower detection rates than the RBPT, both in humans [RR = 0.68 (95% CI: 0.48&ndash:0.96), I2 = 90%] and cattle [RR = 0.41 (95% CI: 0.25&ndash:0.68), I2 = 96%]. Similarly, the complement fixation test (CFT) had a lower detection rate than the RBPT for brucellosis both in cattle [RR = 0.97 (95% CI: 0.94&ndash:0.99), I2 = 9%] and sheep [RR = 0.97 (95% CI: 0.95&ndash:0.99), I2 = 0%]. This meta-analysis demonstrated that, for the screening of brucellosis in both humans and animals, primary binding assays are the preferred diagnostic tools, followed by the RBPT and slow agglutination tests. However, their effective implementation requires context-specific diagnostic strategies and combined testing approaches to enhance accuracy and reliability.