Iranian Journal of

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Background and Objectives: Shift work as a pervasive phenomenon in various industrial sectors is one of the most stressful factors in the workplace. Considering the contradictory reports on the relationship of shift work and hypertension, the main objective of the present study was to investigate the relationship between these two variables among petrochemical industry staff of Mahshahr, Iran.
Methods: In this longitudinal study, 3254 petrochemical staff were investigated during 2008-2011. According to work schedule, shift workers were divided into two groups of shift work and day work (1872 day workers and 1382 shift workers). The aim of this research was to assess the effect of shift work on hypertension by adjusting confounding variables such as gender, age, body mass index, and smoking. The data were analyzed using a random-effects logistic regression model.
Results: Of 3254 (3142 males and 112 females) subjects, 37.85% (860 subject) were hypertensive. The random effects model, with controlling covariates, showed no significant relationship between shift work and hypertension (OR=1.04, 95% CI= (0.98, 1.10). Moreover, the variance of the random effects was significant. 
Conclusion: Generally, according to the results of this study, shift work is not a significant risk factor for hypertension.

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Background and Objectives: Pulmonary embolism is a potentially fatal and prevalent event that has led to a gradual increase in the number of hospitalizations in recent years. For this reason, it is one of the most challenging diseases for physicians. The main purpose of this paper was to report a research project to compare different data mining algorithms to select the most accurate model for predicting pulmonary embolism in hospitalized patients. This model would provide the knowledge needed by the medical staff fir better decision making.
 
Methods: In this research, we designed a prediction model using different methods of machine learning that would best predict the probability of pulmonary embolism in patients at risk. Among data mining algorithms, Bayesian network, decisions tree (J48), logistic regression (LR), and sequential minimal optimization (SMO) were used. The data used in the study included risk factors and past history of patients admitted to the Lung Department of Shariati Hospital, Tehran, Iran.
 
Results: The results showed that the accuracy and specificity of all prediction models were satisfactory. The Bayesian model had the highest sensitivity in predicting pulmonary embolism.
 
Conclusion: Although the results showed a little difference in the performance of prediction models, the Bayesian model is a more appropriate tool to predict the occurrence of pulmonary embolism in hospitalized patients in this type of data. It can be considered a supportive approach along medical decisions to improve disease prediction.

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Background and Objectives: Hypertension is a major global issue due to its consequences. Ordinary regression models have limitations in assessment of hypertension since the reference graph derived from a specific population may not be appropriate for another population. The polynomial quantile regression model is considered as a possible alternative. Hence, this study was conducted with the aim of determining reference values as well as blood pressure percentile curves in Mashhad.
 
Methods: This cross-sectional study was carried out in a random sample of 6949 individuals attending Samen health centers for diabetes screening in 2010. Different percentiles were analyzed using some variables such as gender, age, BMI, WHR, and systolic and diastolic blood pressure. The R software (version 3.0.1) was used for data analysis.
 
Results: In this study, 70.58% and 29.42% of subjects were men and women, respectively. The results of the quantile regression model showed that with an increase in age, BMI, and WHR, blood pressure increased in all percentiles. In all variables, subjects in the 75th and 95th percentiles had moderately high and high blood pressure while they had a normal blood pressure in other percentiles.
 
Conclusion: The model provided more information about blood pressure and its related patterns. According to the results, it seems that more attention should be paid to elderly and overweight individuals in the 75th and 95th percentiles.

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Background and Objectives: Periodontal disease is one of the most common oral health problems. Clinical attachment loss occurs in sever periodontal cases (CAL>3). In this study, we applied a classic regression model and the models that consider the hierarchical structure of the data to estimate and compare the effect of different factors on CAL.
 
Methods: This cross-sectional study was performed in 375 pregnant women and 192 mothers of three-year-old children. The data were gathered from 16 health networks of Shahid Beheshti University of Medical Sciences, Tehran, Iran. CAL was determined for 6 teeth per person by a dentist according to WHO standard oral health examination form. Three-level and ordinary logistic regression analyses were applied for data analysis using the STATA software 14.
 
Results: Of 3,402 examined teeth, 6.3% had CAL> 3mm. Based on the obtained results, the odds of CAL>3mm were 2.4 in the third semester compared to non-pregnant women. The odds of CAL>3mm were 2.86 in women without daily floss use compared to women with routine daily floss use. Posterior teeth were more likely to have CAL>3m than anterior teeth (OR = 1.65) (P-value < 0.05).
 
Conclusion: According to the AIC index, multi-level logistic regression model has a better fit than ordinary logistic regression model and can estimate the coefficients of factors related to CAL>3mm more precisely. The use of the ordinary logistic regression model in hierarchical data can result in underestimated standard errors of the estimated parameters.

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Background and Objectives: The purpose of this study was to predict the mortality rate of colorectal cancer in Iranian patients and determine the effective factors  on the mortality of patients with colorectal cancer using random forest and logistic regression methods.
 
Methods: Data from 304 patients with colorectal cancer registry from the Gastroenterology and Liver Research Center of Shahid Beheshti University of Medical Sciences during the years 2009 to 2014 were used as a retrospective study. Data analysis was performed using random forest and logistic regression methods. To analyze the data, R software version 3.4.3 was considered.
 
Results: Ten important variables related to colorectal cancer deaths were selected by random forest method. Several criteria such as the area under the characteristic curve (AUC) were used to compare the random forest method with logistic regression. According to both criteria, five important variables ranked by random forest were Cancer stage, age of diagnosis, patient's age, HLA, and degree of differentiation (tumor differentiation). In terms of different criteria, the random forest method had better performance than logistic regression (Area under the ROC curve for random forest and logistic regression methods was: 98%; 80% respectively).
 
Conclusion: Variables such as Cancer stage, age of diagnosis, patient's age, HLA, and degree of differentiation are considered as the most important factors affecting mortality in colorectal cancer, that the patients' longevity can be increased with the early diagnosis of cancer and screening programs.
 

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Background and Objectives: Analysis of ordinal data outcomes could lead to bias estimates and large variance in sparse one. The objective of this study is to compare parameter estimates of an ordinal regression model under maximum likelihood and Bayesian framework with generalized Gibbs sampling. The models were used to analyze ovarian hyperstimulation syndrome data.
 
Methods: This study used the data from 138 patients of a clinical trial phase III to compare the efficacy of intravenous Albumin and Cabergoline in prevention of ovarian hyperstimulation syndrome. The original study was done between 2010 to 2011 in Royan institute. We compared maximum likelihood and Bayesian estimation with generalized Gibbs sampling for an ordinal regression model based on confidence intervals and standard errors. The model were fit through R 3.3.2 software version.
 
Results: Markov Chain Monte Carlo results reduced the standard errors for estimates and consequently, narrower confidence intervals. Autocorrelations for generalized Gibbs sampler reached to zero in compare to standard Gibbs sampler for shorter time.
 
Conclusion: It seems that confidence intervals of an ordinal regression model are shorter for generalized Gibbs sampler in compare to standard Gibbs and maximum likelihood. It suggests doing more studies to warrant the results.

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Background and Objectives: an important problem challenging cesarean section is its extensive use as a common method of delivery. Due to the growing trend of cesarean section in Iran in recent years, the natural delivery promotion program was implemented as one the programs incorporated in the Health System Reform Plan in 2014. In this study, the trend of changes in the percentage of CS delivery in Qom Province following the implementation of this program was evaluated.
 
Methods: This trend analysis that was performed in all cesarean deliveries in Qom Province from 2005 to 2018 using a joinpoint regression method.
 
Results: These results showed an annual increase of0.4% in the CS percentage 95% CI: -0.5 to  1.2), which was not statistically significant. A significant decrease was observed in the rate of CS in governmental hospitals. Conversely, in non-governmental hospitals, the percentage of CS increased significantly.
 
Conclusion: According to the findings of this study, after more than 3 years of implementation of health sector evolution plan, overall implementation of this plan failed to significantly reduce the overall process of cesarean delivery during this period in Qom province and achieve the predetermined goals.

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Background and Objectives: Cancer is a complex disease with a lengthy and expensive course of treatment that causes many problems for the community. Knowledge of oral cancer plays an important role in early diagnosis. The aim of this study was to determine the level of knowledge about the symptoms and risk factors of oral cancer and assess the related factors.
 
Methods: In this study, 671 parents of primary school children were randomly selected from primary schools in four districts of Tehran. The participants were asked to answer questions related to demographic characteristics and knowledge of the risk factors and symptoms of oral cancer. Data analysis was done using Poisson regression model and multi-level Poisson regression model using SPSS and STATA software. The AICI Akaike Information Criterion (AIC) was applied to evaluate the models.
 
Results: The mean score of knowledge was 3.7 with a standard deviation of 6.7. Among the studied variables, female gender, advanced age, a higher SES score, and a higher welfare index had positive effects on oral cancer knowledge (P <0.05).
 
Conclusion: The results of this study showed that demographic, social and economic factors of parents were effective on oral cancer. It can be statistically concluded that a multilevel Poisson regression model is more suitable for analyzing this data.
 

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Background and Objectives: Diabetes is considered as one of the most common endocrine disorders worldwide. The aim of this study was to investigate the factors associated with diabetic complications.
 
Methods: A case-control study was performed on the data of 70089 diabetic patients (4622 cases and 53613 controls) extracted from the SINA Electronic Health Record (SinaEHR®) in a population covered by Mashhad University of Medical Sciences in 2018. The effect of independent variables on the likelihood of diabetic complications was investigated using single-variable and multivariate logistic regression models with the control of the potential confounding effects.
 
Results: Using the multivariate logistic regression, the odds of developing diabetic complications were 0.35 (0.31-0.38) for living in the city, 0.73(0.67-0.79) for living in the suburbs and 0.31(0.28-0.33) for living in rural areas relative to the metropolises, 0.84 (0.78-0.91) for illiterate subjects, 0.70 (0.66-0.75) for physical activity, 1.51(1.34-1.71) for stage 1 hypertension and 1.87 (1.43-2.44) for stage 2 hypertension relative to normal blood pressure, 0.79(0.74-0.85) for uncontrolled low density lipoprotein and 1.42(1.33-1.51) for uncontrolled hemoglobin A1C.
 
Conclusion: Various risk factors were identified to increase the odds ratio of diabetic complications. The most important risk factors were uncontrolled glycosylated hemoglobin and stage 1 and 2 hypertension. Control of these factors can reduce the chance of diabetic complications in diabetic patients.
 

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Background and Objectives: The incidence of colorectal cancer has increased significantly in Iran in recent decades. The pattern of occurrence varies in different populations. A study was conducted to perform a spatial analysis of colorectal cancer and some of its risk factors in Iran using GIS.
 
Methods: The data of this descriptive-analytic study included colorectal cancer incidence as a dependent variable and physical activity, Body Mass Index and smoking as independent variables recorded by the Cancer Department, Center for Non-Communicable Diseases Management, the Ministry of Health and Medical Education and the care system for non-communicable disease risk factors according to province and gender in 2009. Data was analyzed using the ArcGIS 10.3 software and spatial correlation analysis, hot spots analysis, and geographic weighted regression model.
 
Results: The spatial relationship between the disease and some of its risk factors was confirmed by the model of geographical weight regression, according to which the northern and central provinces had the highest risk of colorectal cancer compared to other regions of the country.
 
Conclusion: The results of this study showed that spatial analysis could be useful in identifying disease patterns, prioritizing the factors affecting it, and controlling the disease through strategic planning and interventions.

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Background and Objectives: Due to the importance of cutaneous leishmaniasis, the national leishmaniasis project began in 2007 in Iran. The aim of the present study was to evaluate community interventions in changes in the incidence of cutaneous leishmaniasis in Isfahan Province from 2002 to 2018: an Interrupted time series regression analysis.
 
Materials and Methods: The present study was a repeated cross-sectional study. The incidence and 95% confidence interval were used to describe the disease trend. Data were entered into the Excel and analyzed using STATA14 software at a significance level of 5%. Intermittent time series regression analysis was used to evaluate community interventions in changes of leishmaniasis incidence.
 
Results: from 2002 to 2018, the data of 43,904 patients with leishmaniasis was registered in Isfahan Health Centers. The mean (standard deviation) age of the patients was 23.99 (19.03) years. The incidence had a decreasing trend after the interventions in all affiliated cities and the whole province.
 
Conclusion: The preventive intervention programs of the provincial health center have been rather successful and have reduced the incidence of the disease in the years after the intervention, so that despite the large number of confounding and influential factors regarding this disease, preventive intervention programs have led to disease control according to the reported annual incidence.

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