Background Personal protection is crucial for reducing the risk of zoonotic pathogen infection among livestock workers. Investigating the current status of its implementation and associated influencing factors can provide empirical evidence for developing more effective intervention measures. Objective To investigate the current status of personal protection implementation among livestock workers in Xinjiang, China and its influencing factors, providing a reference for formulating targeted intervention measures. Methods This study was conducted in Bayingolin Mongol Autonomous Prefecture, Kashgar region, and the First and Eighth Divisions of Xinjiang Production and Construction Corps. We selected large-scale cattle and sheep farms, cooperatives, individual livestock households, livestock trading markets, slaughterhouses, and retail markets. Using cluster sampling, we recruited all livestock workers (1074 participants) at sampled sites. During the period from April to July 2024, we conducted face-to-face surveys with a self-designed questionnaire among livestock workers, collecting general demographic information, occupational exposure history, and information related to the use of personal protective equipment, resulting in 939 valid questionnaires. EpiData 3.1 was used for data entry and SPSS 26.0 for statistical analysis. We employed logistic regression to compare the implementation of personal protection among workers by selected categories. Results Among the study subjects, there were 300 workers from large-scale farms (31.95%) and 583 self-employed livestock farmers (62.09%). The sample included 600 males (63.90%) and 339 females (36.10%). The majority of the study subjects was farmers, breeders, or aged 18-60 years (87.75%), auxiliary workers (87.97%) and had an education level of junior high school or below (81.36%). The proportion of workers who consistently and correctly implemented all five personal protective measures (washing hands after work, wearing gloves, wearing masks, wearing dedicated work clothes, and using rubber boots) was 35.14% (330/939). The implementation rates for these five protective measures were 93.61% (879/939), 58.68% (551/939), 57.93% (544/939), 46.01% (432/939), and 42.81% (402/939), respectively. There were statistically significant differences in the implementation rates of differen personal protective measures among the workers by occupation and education level (P<0.05). Among them, workers with lower education levels had lower implementation rates for various protective measures. The results of logistic regression indicated that, compared to male workers, female workers (OR=2.26, 95%CI: 1.68, 3.04) showed a higher rate of non-standard implementation of personal protection. Using the age group of 18 to less than 36 years as a reference, those aged 36 to less than 60 years (OR=1.82, 95%CI: 1.33, 2.50) and 60 years or older (OR=4.45, 95%CI: 2.56, 7.73) reported higher rates of non-standard implementation of personal protection. Compared to those with an education level of primary school or below, those with a junior high school education (OR=0.64, 95%CI: 0.47, 0.88) and a high school education or above (OR=0.13, 95%CI: 0.19, 0.29) showed lower rates of non-standard implementation of personal protection. Compared to farmers, veterinarians (OR=0.03, 95%CI: 0.02, 0.07) reported the lowest rate of non-standard implementation of personal protection, followed by auxiliary workers (OR=0.08, 95%CI: 0.05, 0.12), breeders (OR=0.10, 95%CI: 0.07, 0.15), and other occupational groups (OR=0.28, 95%CI: 0.15, 0.52). Using large-scale farms as a reference, self-employed farmers (OR=13.5, 95%CI: 9.65, 18.88) reported a higher rate of non-standard implementation of personal protection, followed by workers from other workplaces (OR=3.63, 95%CI: 2.01, 6.54). Conclusion The implementation of personal protective measures among livestock workers in Xinjiang is generally satisfactory. However, significant disparities in the execution of these measures exist among different types of workers, indicating the need to enhance occupational health promotion to reduce the risk of zoonotic pathogen infection among the workers.

Background Animal farming may affect the structure and diversity of gut microbiota of farm workers, but it needs more studies to provide solid evidence. Objective To analyze the diversity characteristics of gut microbiota in dairy farm workers, dairy cows, and the control population (non-animal contact occupational group), and to assess the impact of dairy farming on the gut microbiota of workers. Methods The 16S rRNA full-length amplicon sequencing technology was used to sequence 60 fecal samples from dairy farm workers, 89 from dairy cows, and 50 from the general population. The gut microbiota structure characteristics, including operational taxonomic units (OTUs), alpha diversity, beta diversity, and the composition of species at the phylum, family, and genus levels were analyzed. The differences in gut microbiota among the three groups of samples were compared to explore the impact of occupational exposure on the gut microbiota structure of dairy farm workers. Results A total of 50478 OTUs were identified in the gut microbiota of farm workers, dairy cows, and the general population, with 23613, 42723, and 16592 OTUs, respectively. The number of shared OTUs between the dairy cows and the general population was 12674 (27.17%), between the general population and the farm workers was 10099 (33.54%), and between the dairy cows and the farm workers was 17690 (36.36%). Compared with the general population, the farm workers and dairy cows showed more shared gut microbiota (P<0.01). The abundance of Firmicutes in the gut of the farm workers was higher than that of the general population (P<0.01), while Actinobacteriota and Proteobacteria were the opposite (P<0.01). The alpha diversity of gut microbiota in the farm workers was different from that of the general population. The ACE index of the farm workers was slightly lower than that of the general population and much lower than that of dairy cows (P<0.05), while their Shannon index was higher than that of the general population but lower than that of dairy cows (P<0.01). The linear discriminant analysis effect size (LefSe) showed that the abundances of Lachnospiraceae and Ruminococcaceae in the gut microbiota of the farm workers were significantly higher than those of the general population, while Streptococcaceae, Lactobacillaceae, Bifidobacteriaceae, Enterobacteriaceae, and Erysipelatoclostridiaceae were lower than those of the general population. Conclusion The dairy farm workers shares a certain proportion of common bacterial communities with dairy cows. Dairy farming operations can affect the structure and diversity of the gut microbiota of workers by increasing the abundances of Lachnospiraceae and Ruminococcaceae, while decreasing the abundances of Streptococcaceae, Lactobacillaceae, Bifidobacteriaceae, Enterobacteriaceae, and Erysipelatoclostridiaceae. The study results provide reference data for further exploring the cross-host transmission of pathogens related to occupational exposure in dairy farming.