Brucellosis seroprevalence in cattle in China during 2014-2024: a systematic review and meta-analysis

Abstract

Brucellosis, caused by several species of Brucella, continues to be a significant illness that poses a global threat to public health. China remains a persistent hotspot for brucellosis, despite the implementation of extensive control measures. This study aims to conduct a systematic review and meta-analysis of the seroprevalence of bovine brucellosis in different breeds and regions of China from 2014-2024, and to provide predictions on the future prevalence patterns of brucellosis in cattle and humans. The analysis comprised a total of 80 research studies, which consisted of 187 datasets and a combined sample size of 3,130,706. We estimated the overall pooled seroprevalence of bovine brucellosis in China to be 1.5% (95% CI: 0.6-2.6%). Subgroup analysis revealed that the seroprevalence in dairy cattle was 3.1%, surpassing the seroprevalence in beef cattle (1.3%) and yak (1.5%). Regions that had authorized vaccination programmes exhibited higher seroprevalence (1.8%) compared to regions that did not have vaccination (0.5%). Notably, the study observed a simultaneous rise in both the prevalence of brucellosis in cattle and the number of human brucellosis cases. This suggests that high-quality routine surveillance of brucellosis in cattle will be essential for predicting and responding to cases in humans. Additionally, given the existing prevention and control measures, brucellosis will likely continue to be prevalent in both cattle and people. This systematic review will assist policymakers in adjusting animal surveillance and interregional livestock movement policies, ultimately contributing to the public safety goal of preventing brucellosis in humans by controlling it in animals.

Keywords: Brucella; cattle; chin; epidemiology; prevalence.

Figure 1.

PRISMA flow diagram for systematic review depicting phases of identification of studies.

Figure 2.

Forest plot of brucellosis seroprevalence in cattle in China. The data were weighted using a random effects model. Each blue square represents the effect size (ES) for individual studies, the horizontal black lines indicating the 95% confidence interval (CI). The vertical dashed line indicates the overall pooled effect size, and the diamond at the bottom represents the overall summary effect. “m” represents the total number of samples, “n” represents the number of positive samples, and “ES” represents to the seroprevalence of brucellosis in cattle in each study.

Figure 3.

Map of seroprevalence of bovine brucellosis in China. The map was created using a standard map from the website of the Ministry of Natural Resources of China, with drawing check No.: GS(2023)2764. The base map has not been modified. The inset depicts the territorial waters of the South China Sea.The map displays the seroprevalence rates (with 95% confidence intervals) in various provinces, using a colour gradient to indicate different prevalence levels. Darker red shading corresponds to higher seroprevalence, ranging from 0.01% to 22.1%. Provinces without available data are represented with diagonal hatching.

Figure 4.

Trends of bovine brucellosis seroprevalence and human brucellosis cases in China (2014-2024). The graph uses dual Y-axes, with the left Y-axis representing bovine brucellosis seroprevalence (%) and the right Y-axis representing reported human brucellosis cases. The purple-square-line represents the national bovine brucellosis seroprevalence, the red-circle-line shows the seroprevalence in high-baseline areas, the blue-triangle-line indicates the seroprevalence in low-baseline areas, and the graysquare-line represents the number of reported human brucellosis cases.

Figure 5.

Prediction of the seroprevalence of brucellosis in cattle and human case numbers based on time series models. ARIMA model prediction (2014-2027) of A: bovine brucellosis seroprevalence at national level. B: bovine brucellosis seroprevalence in high-baseline area, C: bovine brucellosis seroprevalence in low-baseline area. D: Human brucellosis cases in China. Abbreviations: Fit (The line that represents the fitted model to the observed data), UCL (Upper Control Limit), LC (Lower Control Limit).