Iranian Journal of

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Background & Objectives: Pediculosis is a ubiquitous and contagious parasitic dermatosis. Throughout the world, infestation by the head louse (Pediculus humanus capitis) is more common among schoolchildren, especially in those aged 6-11 years. This descriptive/analytical study was carried out in 2003 to determine the prevalence of pediculosis capitis and risk factors involved in the epidemiology of pediculosis in primary school pupils in Amlash, Gilan province.
Methods: The children were selected by cluster random sampling of schools and classes, and then examined for head lice using hair conditioners and a fine-toothed head lice comb. We screened a total of 4244 pupils from 95 primary schools. We used a comprehensive questionnaire to evaluate the effects of school type and location, education level, parent job, regular baths, number of persons sharing the same bed, combing frequency, host sensitivity, family size, hairstyle, gender, and hair length.
Results: Statistical analysis using χ2 tests showed that school type, school location, parent job, regular baths, number of person who share the same bed, frequency of combing, gender, host sensitivity, family size, and hairstyle are significantly associated with infestation. Infestation rate showed no correlations with hair color, season, age, or education level. Results showed that pediculosis is more prevalent in rural than in urban areas. The average rate of infestation for the area was 9.2%, indicating an epidemic situation by standard criteria.
Conclusions: The high prevalence of P. capitis infestation among these children was probably due to poor environmental hygiene and scarcity of water. Hygienic controls of schoolchildren by nurses are important for elimination of Pediculus humanus capitis. This is the first community-based study describing in detail the epidemiology of head louse infestation in the Amlash district of Gilan province in Iran and confirms that Pediculosis capitis is still a problem in many environments, particularly those with low life standards and poor health care.

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Background & Objective: Using parametric models is common approach in survival analysis. In the recent years, artificial neural network (ANN) models have increasingly used in survival prediction. The aim of this study was to predict of survival rate of patients with gastric cancer by using a parametric regression and ANN models and compare these methods.
Methods: We used the data of 436 gastric cancer patients from a cancer registry in Tehran between 2002-2007. All patients had a confirmed diagnosis. Data were randomly divided into two groups: training and testing (or validation) set. For analysis of data we used a parametric model (exponential, Weibull, normal, lognormal, logistic and log-logistic models) and a three layer ANN model. In order to compare of the prediction of two models, we used the area under receiver operating characteristic (AUROC) curve, classification table and concordance index.
Results: The prediction accuracy of the ANN and the parametric (Weibull) models were 79.45% and 73.97% respectively. The AUROC for the ANN and the Weibull models were 0.815 and 0.748 respectively.
Conclusions: The ANN had a better predictions than the Weibull model. Thus it is suggested to use of the ANN model survival prediction in field of cancer.

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Background & Objectives: Cholera is always being considered as a public health threat in poor and developing countries. However outbreaks of cholera are not very common in central area of Iran in 2008 district health authority reported a cluster of diarrhea cases. We investigated this cluster to identify the etiological agent, source of transmission and propose control measures.
Methods: We analyzed the data of total of 1219 patients with colorectal cancer who registered between 1 January 2002 to 1 October 2007. Data were analyzed using univariate and multivariate Accelerated Failure Time (AFT) parametric survival model with frailty, utilizing STATA statistical software.
Results: In the univariate analysis for age at diagnosis, gender, marital status, race and education level, the survival of patients with colon cancer were approximately between half to one fourth and for BMI, alcohol history, Inflammatory Bowel Disease (IBD), familial history of cancer and the pathologic stage of tumor, the survival of patients with colon cancer were significantly (between 0.12 to 0.56 times) shorter than those patients with rectal cancer. In the multivariate analysis, for age at diagnosis (45-65 years), there was significant difference between colon and rectum cancer. But for BMI, alcohol history, IBD and pathologic stage there were not significant differences. The adjusted survival and 1, 2, 3, 4 and 5 year survival of patients with rectal cancer were better than those with colon cancer.
Conclusions: Site-specific evaluation of colon and rectum could give a better perspective of factors affecting these cancers. It may help to design of clinical trials, better diagnosis of diseases and optimal administration of specific treatments.

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Background & objectives: Competing risk data is one of the multivarite survival data. Competing risk data can be modelled using copula function. In this study we propose a bayesian modelling approach of competing risk data using the copula function.
Methods: We used the data from colorectal cancer registyrarty in Tehran. After constructing likelihood function using Clayton copula by choosing appropriate prior distribution for parameters, we obtained the posterior distribution of parameters using the Metropolis-Hastings algorithms and Slice sampling.
Results: The results of univariate analysis showed that sex, histology of tumor, extent of wall penetration, lymph node metastasis, distant metastasis and pathological stage of tumor were significantly associated with colon cancer and sex, histology of tumor, lymph node metastasis, distant metastasis and pathological stage of tumor were were significantly related to rectal cancer. In the multivariate analysis, age at diagnosis, tumor grade and distant metastasis were significant prognostic factors for colon cancer and tumor grade and size of the tumor were significant prognostic factors of rectal cancer
Conclusions: As we showed some variables may have different impacts on colon and rectum cancers, consequently, further studies are needed to be conducted considering risk factors of these cancers separately.

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Background & Objective: Mixed outcomes arise when, in a multivariate model, response variables measured on different scales such as binary and continuous. Artificial neural networks (ANN) can be used for modeling in situations where classic models have restricted application when some of their assumptions are not met. In this paper, we propose a method based on ANNs for modeling mixed binary and continuous outcomes.
Methods: Univariate and bivariate models were evaluated based on two different sets of simulated data. The scaled conjugate gradient (SCG) algorithm was used for optimization. To end the algorithm and finding optimum number of iteration and learning coefficient, mean squared error (MSE) was computed. Predictive accuracy rate criterion was employed for selection of appropriate model. We also used our model in medical data for joint prediction of metabolic syndrome (binary) and HOMA-IR (continues) in Tehran Lipid and Glucose Study (TLGS). The codes were written in R 2.9.0 and MATLAB 7.6.
Results: The predictive accuracy for univariate and bivariate models based on simulated dataset Ι, where two outcomes associated with a common covariate, were shown to be approximately similar. However, in simulated dataset ΙΙ in which two outcomes associated with different covariates, predictive accuracy in bivariate models were seen to be larger than that of univariate models.
Conclusions: It is indicated that the predictive accuracy gain is higher in bivariate model, when the outcomes share a different set of covariates with higher level of correlation between the outcomes.

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Background & Objectives: Changing the pattern of mortality gives important perspective of health determinants. The aim of this study is to detect location and time of mortality pattern change in country using statistical change point method during 1971-2009 Years.
Methods: We assume for years before and after 0 k , t y has a Poisson distribution with means 0 l and 1 l , respectively. We used several methods for estimation change point in real data by assume Poisson model.
Results: Using two simulated and real data analysis showed that the change point has been occurred in year 1993 and this confirmed by all methods.
Conclusion: Our findings have shown that the change pattern of mortality trend in Iran is related to improvement of health indicators and decreasing mortality rate in Iran.

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Background & Objectives: Study trend of observed rates changes provide valuable information for need assessment, plan, reload programs and develop indicators of each country. The main objective of this paper is to determine the changes in tuberculosis incidence rate trend in Iran by applying segmented regression model.
Methods: In this study, segmented Linear Regression employed to analyze the trend of changes in pattern of Tuberculosis incidence rate during past 44 years (1964-2008) in Iran. We used least square method and permutation test and Bayesian Information Criteria to decide which of the two segment regression model and poison regression would be better. Data analyzed by Joinpoint3.4 and SAS9.1 software. Results: According the permutation test, it was detected that there were two breakpoints over 1977 and 1993 years (p=0.0108). Incidence rate of tuberculosis during the first 11 years of review had declined with annual percentage change = -10.1%, for second segment it rose upward with 4.3% increase in per year and for end segment TB incidence rate again declined with annually 4.5%. The average annual change of Tuberculosis incidence rate in Iran for at least 10 years has been estimated -4.5 percentages.
Conclusion: The findings of this study have shown that the incidence rate of Tuberculosis decreased after 1992 that interestingly this decline seems faster than estimated by international TB control program. This indicates that preventive and treatment of Tuberculosis programs have been successful in Iran.

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Background and Objectives: Since there is inconsistency reports in relationship between shift work (SW) and blood pressure (BP), therefore we aimed to show any association between SW and BP by using of Bayesian Multilevel Modeling, which is a reliable method for this type of analysis.
 Methods: The profiles of 4145 workers in Polydactyl Iran Corporation were examined in historical cohort between 1996 until 2008. All relevant analysis was performed by Win Bugs software.
Results: Approximately 98 percent of study population was male. Of total 1886 (45.5%), 307(7.4%), 1952 (47.1%) of participation were day worker, two rotation shift worker and three rotation shift worker respectively. After controlling confounding factors, there was no significant relationship with Systolic BP (P=0.911) and Diastolic BP (P=0.278).
Conclusion: In general, the results of our historical cohort study do not support a relationship between SW and BP. We suggest multi center and prospective cohort studies with controlling more confounding factors in this area.

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  Background and Objectives : Previous studies have reported contradictory results regarding the association of Shift Work (SW) and Blood Cholesterol (BC). In this paper, we studied the relationship between SW and BC.

  Methods: The data of this historical cohort study was extracted from annual observations of the workers of Esfahan’s Mobarakeh Steel Company selected through cluster random sampling between 1996 and 2011. In this research, we assessed the effect of SW on BC with controlling BMI, age, work experience, marital status, smoking, and educational status.

  Results : Five hundered and seventy four male workers participated in this study with a mean (SD) age of 41.89 (7.51) and mean (SD) work experience of 16.75 (7.16) years. In this study, after controlling confounding factors, we found no significant relationship between SW and BC.

  Conclusion: Because our study showed no relationship between SW and BC, we can state that this relationship does not exist with more certainty.

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Background and Objectives: Shift work could threaten health in the long term. The present research aimed to assess the association between shift work and body mass index (BMI) using the multilevel (hierarchical) model during a particular period of time.
Methods: The data of this longitudinal study were collected from a sample of Esfahan’s Mobarakeh steel and Polyacryl companies personnel during 2008 to 2011. Shift work schedule included day work and rotational shift work. The multilevel regression model was utilized for analysing the data and assessing the effect of shift work on BMI by controlling confounding variables including marital status, work expectation, age, company, and educational level.
Results: In this study, of 1368 workers, 42.3% (n=578) and 57.7% (n=790) were day workers and rotating shift workers, respectively. The mean (±SD) age of the day workers and rotating shift workers was 33.07 (±8.66) years and 33.31 (±8.70) years, respectively. After adjusting for confounding variables in a two-level hierarchical model, the association between shift work and BMI was not statistically significant (P=0.837). About 90% of total variation was related to personnel.
Conclusion: According to the results of the present study, no statistically significant relationship was found between shift work schedule and BMI. Thus, other similar studies with a longer follow up period (more than four years) and controlling more confounder factors are necessary to evaluate the relationship between shift work and BMI more accurately.

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Background and Objectives: Gestational diabetes mellitus (GDM) is a medical problem in pregnancy, and its late diagnosis can cause adverse effects in the mother and fetus. The purpose of this research was to estimate the accuracy parameters of a biomarker for early prediction of gestational diabetes in the absence of a perfect reference standard test.
 
Methods: This study was conducted in 523 pregnant women who presented to Mahdieh Hospital and Taleghani Hospital affiliated with Shahid Beheshti University of Medical Sciences, Tehran, Iran 2017-2018. As a predictor for detecting GDM, beta- human chorionic gonadotropin (β-hCG) measurements were recorded during 14-17th weeks’ gestation in a checklist. The Bayesian latent variable model was used to estimate the sensitivity, specificity, and area under receiver operating characteristic curve (AUC). Bayesian parameter estimation was calculated using the R2OpenBUGS package in R version 3.5.3.
 
Results: The median gestational age was 33 years. In the absence of a perfect reference test, the applied model had a sensitivity, specificity, and AUC of 78% (95% credible interval (CrI): 0.66-0.83), 83% (95% CrI: 0.74-0.89), and 0.72 (95% CrI: 0.64-0.88) for β-hCG, respectively. 
 
Conclusion: According to the results of this study, β-hCG may be an acceptable biomarker for early diagnosis of diabetes in pregnant women in the absence of a perfect reference test.