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Background: The effective use of limited health care resources is of prime importance. Assessing the length of stay (LOS) is especially important in organizing hospital services and health system. This study was conducted to identify predictors of LOS among patients who were admitted to a general surgical unit. Methods: In this cross-sectional study, the sample included all patients who were admitted to the general surgical unit of Shariati hospital in 2013 (n= 334). To determine the factors affecting LOS, Zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and zero-inflated generalized Poisson (ZIGP) regression models were fitted using R software, and then the best model was selected. Results: Among all 334 patients, the mean (±SD) age of the patients was 45.2 (±16.47) years and 220 (65.9%) of them were male. The results revealed that based on ZIGP model, type of surgery (appendicitis, abdomen and its contents, hemorrhoids, lung, and skin), type of insurance, comorbid diseases (hypertension, heart disease, and hyperlipidemia), place of residence (local and non-local), age, and number of tests had significant effects on the LOS of GS patients. Conclusion: According to the Akaike information criterion (AIC) in each fitted model, it was found that ZIGP regression model is more appropriate than ZIP and ZINB regression models in assessing LOS in GS patients, especially due to the presence of excess zeros and overdispersion in count data.

Background: An important feature of Poisson distribution is the equality of mean and variance. However, additional zeroes in the data may cause over-dispersion in most cases, in which zero-inflated models are recommended. In this study, we aimed to evaluate the efficacy of zero-inflated models to predict hospital length of stay (LOS) using real data and simulated study. Methods: This study was conducted on patients admitted at Shariati hospital, Tehran, Iran. Zero inflated Poisson (ZIP), zero inflated negative binomials (ZINB) and zero inflated generalized Poisson (ZIGP) models were fitted on patient's length of stay. The fitted models were compared using the Akaike information criterion (AIC). The simulated data was generated using a model with the lowest AIC. Different models were then compared using the AIC. Data analysis was performed in R statistical software. Results:The results of both real data and simulation study showed lower AIC for ZIGP model compared to ZIP and ZINB model. Conclusion: Given the high dispersion and Zero Inflation in hospital LOS, the zero-inflated generalized Poisson regression model is the most suitable model to predict determinants of LOS. Keywords: Computer Simulation, Hospital, Length of Stay, Poisson Distribution Citation: Farhadi Hassankiadeh R, Kazemnejad A, Gholami Fesharaki M, Kargar Jahromi S. Efficiency of zero-inflated generalized poisson regression model on hospital length of stay using real data and simulation study.

Background: Although vaginal delivery is the safest type of childbirth, cesarean section (CS) without any medical indication is currently increasing in the world, especially in Iran. The purpose of this study was to determine the type of delivery and its related factors in women working in the departments of Guilan University of Medical Sciences. Methods: This cross-sectional study recruited 100 women employed in the departments of Guilan University of Medical Sciences in 2017. Data were collected using a questionnaire including demographic and reproductive details of all participants and the reason for choosing CS among women with previous CS. Fisher's exact test and Chi-square test were used to determine the factors related to delivery type. Results: The prevalence of cesarean section in this study was 80%. Older age at pregnancy and higher education of the respondent and her husband was significantly associated with higher rate of CS. Spouse and relative suggestion for normal delivery was associated with lower rate of CS. The main reasons for CS were women's fear of childbirth, labor pain, and physician's recommendation. Conclusion: The rate of CS delivery is very high in working women. Since concern about pain and possible damage to the body was the most important reasons of choosing CS, providing training classes, better facilitation for normal delivery and adding a special course for girls in high school education is recommended to develop a positive attitude toward normal delivery in women.

Background: Upper limb musculoskeletal disorders are a health problem among in dental jobs. The aim of the study was to investigate of upper limb musculoskeletal disorders among dental jobs. Methods: A cross-sectional study was carried out among 190 in dental jobs of in Tehran cities of Iran in 2016. A modified Nordic questionnaire with interview was used to collect data on individual characteristics and musculoskeletal disorders, Univariate analyses and multiple logistic regression analysis were then performed. Results: In this study dental jobs were participated with age (mean ±SD) 33.56±9.33 yrs., duration of employment 10.19 ±8.85. Prevalence rate of reported upper limb musculoskeletal disorders in each body site was 72.4% in previous 12-month. The most prevalent musculoskeletal complaints was neck pain (33.3%). Significant relations were found between occurrence of upper limb musculoskeletal disorders and age, gender, heavy work (P-value<0.05). Conclusion: For important action in reduce Upper limb musculoskeletal disorders, design station works by ergonomics of standards and exercise often work are suggested. Keywords: Limb Musculoskeletal Disorders, Dental Jobs