BackgroundThe objectives of this study are to investigate the incidence and reporting behavior of sharp injuries among healthcare workers (HCWs) and identify the risk factors associated with these injuries.MethodsA cross-sectional survey was conducted in February 2017 in a provincial teaching hospital in China. Data were collected from 901 HCWs using a self-administered questionnaire which included demographic information, experience, and reporting behavior of sharp injuries. Stepwise logistical regression was used to analyze the risk factors.ResultsHCWs (248 [27.5%]) had sustained a sharp injury in the previous year. Factors including seniority, job category, title, education, department, and training programs were associated with the occurrence of sharp injuries. According to the stepwise logistical regression, seniority, and training programs were the risk factors associated with the occurrence of sharp injuries. Of 248 sharp injuries, 130 HCWs were exposed to blood. Only 44 (33.9%) HCWs reported their injuries to the concerned body. The main reasons for not reporting the sharp injuries were as follows: perception that the extent of the injury was light (30.2%), having antibodies (27.9%), and unaware of injury (16.3%).ConclusionsSharp injuries in the studied hospital were common and were likely to be underreported. Therefore, an effective reporting system and sufficient education on occupational safety should be implemented by the relevant institutions. Moreover, it is important to take effective measures to manage sharp injuries in HCWs and provide guidance for their prevention.
Background Autism is a lifelong disability associated with several comorbidities that confound diagnosis and treatment. A better understanding of these comorbidities would facilitate diagnosis and improve treatments. Our aim was to improve the detection of comorbid diseases associated with autism. Methods We used an FP-growth algorithm to retrospectively infer disease associations using 1488 patients with autism treated at the Guangzhou Women and Children’s Medical Center. The disease network was established using Cytoscape 3.7. The rules were internally validated by 10-fold cross-validation. All rules were further verified using the Columbia Open Health Data (COHD) and by literature search. Results We found 148 comorbid diseases including intellectual disability, developmental speech disorder, and epilepsy. The network comprised of 76 nodes and 178 directed links. 158 links were confirmed by literature search and 105 links were validated by COHD. Furthermore, we identified 14 links not previously reported. Conclusion We demonstrate that the FP-growth algorithm can detect comorbid disease patterns, including novel ones, in patients with autism.
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