Epidemiology and risk factors of needlestick injuries among healthcare workers in Iran: a systematic reviews and meta-analysis

BACKGROUND@#Occupational contact with blood and body fluids poses a significant risk to healthcare workers. The aim of this systematic review is to investigate the epidemiology and risk factors affecting needlestick injuries (NSI) in healthcare personnel in Iran.@*METHODS@#In March 2020, researchers studied six international databases such as Medline/PubMed, ProQuest, ISI/WOS, Scopus, Embase, and Google Scholar for English papers and two Iranian databases (MagIran and SID) for Persian papers. Joanna Briggs Institute (JBI) Critical Appraisal Checklist was used to assess quality of studies. The method of reporting was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement.@*RESULTS@#A total of 43 articles were included in the analysis. Results showed that females (OR = 1.30, 95 % CI 1.06-1.58, P value = 0.009), younger age (OR = 2.75, 95 % CI 2.27-3.33, P value < 0.001, rotated shift workers (OR = 2.16, 95 % CI 1.47-3.15, P value < 0.001), not attending training courses (OR = 1.30, 95 % CI 1.07-1.56, P value = 0.006), working in the surgery ward (OR = 1.83, 95 % CI 1.33-2.50, P value < 0.001), less work experience (OR = 1.43, 95 % CI 1.04-1.95, P value = 0.025) apposed a greater risk factors for NSI among healthcare workers.@*CONCLUSION@#Based on the results of this review, factors such as young age, less work experience, work shift, and female gender are considered as strong risk factors for NSI injury in Iran. Preventive measures including education programs can reduce the burden of NSI among healthcare personnel.

Presentation of a model-based data mining to predict lung cancer

Background: The data related to patients often have very useful information that can help us to resolve a lot of problems and difficulties in different areas. This study was performed to present a model-based data mining to predict lung cancer in 2014

Methods: In this exploratory and modeling study, information was collected by two methods: library and field methods. All gathered variables were in the format of form of data transferring from those affected by pulmonary problems [303 records] as well as 26 fields including clinical and environmental variables. The validity of form of data transferring was obtained via consensus and meeting group method using purposive sampling through several meetings among members of research group and lung group. The methodology used was based on classification and prediction method of data mining as well as the method of supervision with algorithms of classification and regression tree using Clementine 12 software

Results: For clinical variables, model's precision was high in three parts of training, test and validation. For environmental variables, maximum precision of model in training part relevant to CandR algorithm was equal to 76%, in test part relevant to Neural Net algorithm was equal to 61%, and in validation part relevant to Neural Net algorithm was equal to 57%

Conclusion: In clinical variables, C5.0, CHAID, C and R models were stable and suitable for detection of lung cancer. In addition, in environmental variables, C and R model was stable and suitable for detection of lung cancer. Variables such as pulmonary nodules, effusion of plural fluid, diameter of pulmonary nodules, and place of pulmonary nodules are very important variables that have the greatest impact on detection of lung cancer