Suchergebnisse

Introduction: Today, one of the most important causes of morbidity and mortality among people all across the world is Injury. Injuries pose an important challenge for the health system and account for 28% of the deaths per day in Iran. Iran is in the 5th place for road traffic death rates globally.
Objective: This was a cross sectional study aimed to estimate annual incidence rates of injury in individuals above 15 in Kashan, Iran.
Methods: In this population-based cross-sectional study, people above 15 years in households residing in Kashan during 2018-2019 were studied The sample had a twofold design, Stratified Cluster Sampling method and network Scale-up method. Data analysis was performed using SPSS 22 (Chi-square and T-test and Logistic regression analysis) and Stata 14 software.
Results: This study describes the study setting, sampling, data collection and management, quality control and assurance, and the statistical analysis. In this study, the annual incidence rate from all kinds of trauma was estimated at 70.61(62.60-78.70) per 1000 person-years, and the death caused by injury was 4.63(34.32-56.12) per 1000 person-years. In addition, in network scale-up method incidence rate of all kinds of trauma was 57.1 and the death caused by trauma was 3.47 per 1000 person-years.
Conclusion: population-based studies were used to evaluate injuries and outcomes of trauma, so that managers realize where attention is needed regarding preventive programs in this city. Using indirect sampling techniques can be cost-effective and time-consuming, and can provide us with the information urgently needed.

Background: One of the challenges when transitioning from International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) to International Classification of Diseases, 11th Revision (ICD-11) is to ensure clinical coding accuracy. Objective: To determine the accuracy of clinical coding with ICD-11 in comparison with ICD-10 and identify causes of coding errors in real clinical coding environments. Method: The study was conducted prospectively in two general hospitals. Medical records of discharged inpatients were coded by hospital clinical coders with both ICD-11 and ICD-10 on different days. These medical records were recoded by five mentors. Codes assigned by mentors were used as the gold standard for the evaluation of accuracy. Results: The accuracy of ICD-10 and ICD-11 coding for 1578 and 2168 codes was evaluated. Coding accuracy was 89.1% and 74.2% for ICD-10 and ICD-11. In ICD-11, the lowest accuracy was observed in chapters 22 (injuries), 10 (ear) and 11 (circulatory) (51.1%, 53.8% and 62.7%, respectively). In both ICD-10 and ICD-11, the most important cause of the coding errors was clinical coders' mistakes (79.5% and 81.8% for ICD-10 and ICD-11, respectively). Conclusion: Accuracy of clinical coding with ICD-11 was lower relative to ICD-10. Hence, it is essential to carry out initial preparations, particularly the training of clinical coders based on their needs, as well as the necessary interventions to enhance the documentation of medical records according to ICD-11 before or simultaneous with the country-wide implementation. Implications: Clinical coders need complete training, especially in using extension codes and post-coordination coding. Local ICD-11 guidelines based on the needs of local users and reporting policies should be developed. Furthermore, documentation guidelines based on ICD-11 requirements should be developed.