Iranian Journal of

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Background & Objectives: Breast cancer is a common malignancy in women in many parts of the world. The incidence of breast cancer in Iranian women is growing. Iranian patients are relatively younger than their western counterparts. We conducted a case-control study to determine roles of reproductive factors for breast cancer among women in Iran.
Methods: A hospital based case-control study was conducted in 2004 at a teaching hospital in Tehran, Iran. A total of 303 cases of breast cancer and 303 healthy controls were interviewed. Cases were identified through the oncology department of a university hospital and controls were recruited from other wards or out-patients clinics at the same hospital. Controls were matched to cases on age. Demographic and reproductive data were ascertained by personal interview using a structured questionnaire. Informed consent was obtained from cases and controls. Odds ratios and 95% confidence intervals for breast cancer were derived using logistic regression analysis.
Results: Mean ±SD age of cases and controls was 48.8 ±9.8 and 50.2 ± 11.1 years, respectively (range 24-84). The final model constructed after multivariate analysis indicated that factors such as never being married, menopause, older age at first live birth, parity, use of oral contraceptives, and history of chest X-ray between adolescence and the age of 30 yrs were significantly associated with breast cancer. Variables such as higher education, early age at menarche, abortion, breast feeding and its duration did not constitute significant risk factors.
Conclusions: Marital status, history of chest X-ray between adolescence and the age of 30, number of live births, and age at first live birth, menopause, and oral contraceptive use seem to increase the risk of developing breast cancer among women in Iran.

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Background and Objectives: Logistic regression is one of the most widely used generalized linear models for analysis of the relationships between one or more explanatory variables and a categorical response. Strong correlations among explanatory variables (multicollinearity) reduce the efficiency of model to a considerable degree. In this study we used latent variables to reduce the effects of multicollinearity in the analysis of a case-control study.
Methods: Our data came from a case-control study in which 300 women with breast cancer were compared to 300 controls. Five highly correlated quantitative variables were selected to assess the effect of multicollinearity. First, an ordinary logistic regression model was fitted to the data. Then, to remove the effect of multicollinearity, two latent variables were generated using factor analysis and principal components analysis methods. Parameters of logistic regression were estimated using these latent as explanatory variables. We used the estimated standard errors of the parameters to compare the efficiency of models.
Results: The logistic regression based on five primary variables produced unusual odds ratio estimates for age at first pregnancy (OR=67960, 95%CI: 10184-453503) and for total length of breast feeding (OR=0). On the other hand, the parameters estimated for logistic regression on latent variables generated by both factor analysis and principal components analysis were statistically significant (P<0.003). The standard errors were smaller than with ordinary logistic regression on original variables. The factors and components generated by the two methods explained at least 85% of the total variance.
Conclusions: This research showed that the standard errors of the estimated parameters in logistic regression based on latent variables were considerably smaller than that of model for original variables. Therefore models including latent variables could be more efficient when there is multicollinearity among the risk factors for breast cancer.

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Background & Objectives: Tobacco smoking is known to cause a huge burden of disease throughout the world: smoking is responsible for 4 million deaths per year. This study is performed in order to fill the existing gaps in information regarding current trends for lifestyle and tobacco-related disease in Iran.
Methods: This was a cross-sectional study performed with the help of a questionnaire and using a multi-stage cluster sampling method. The final sample size, after the screening of the data, was 84706 out of 89457 records.
Results: From a total of 84706 people in this study, 49.7% were men, 52% were uneducated and 64.8% cane from urban areas 79.9% did not smoke, 15.3% smoked actively, 2.1% were non-active smokers and 2.7% were ex-smokers. The prevalence of smoking was higher in people aged 35 to 64 years (P<0.001). Quitting rates increased with age, especially in women (P<0.001). Smoking was more prevalent in men (P<0.001) and in rural areas (P<0.001). The average number of cigarettes smoked per day was 14.69. Smoking prevalence was lower among adults with higher levels of education compared to those with lower education levels (P<0.001).
Conclusions: This nationwide survey provides a baseline for future longitudinal studies of smoking in Iran. There is a need for effective smoking prevention and cessation programs with a focus on the young population.

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Background & Objectives: There is geographic variability in the prevalence of asthma. Since there is inadequate of study on asthma in Iran, this study was performed in order to determine the prevalence of asthma in Kurdistan province.
Methods: This cross-sectional study was performed as part of Iranian national survey to determine the risk factors of non communicable disease.
Results: This study includes 500 men and 500 women. Prevalence of history of asthma was 2.3%. There was no statistically significant association between asthma and place of residence, smoking and body mass index. Asthma were found in 6 (1.8%) of men and 17(5%) of women (p=0.02). Prevalence of asthma increased with increasing age (p<0.001)
 Conclusions: The prevalence of asthma in this study was similar with other studies in Iran and seems lower than similar countries.

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Background and objective: Numerous studies have reported beneficial effects of smoking cessation in terms of decreased cardiovascular mortality in patients with coronary heart disease. This paper aimed to determine a valid estimate for the relative risk of mortality in subjects who quit smoking compared to those continued smoking.
Methods: All relevant prospective cohort studies of chronic heart disease published during 1975 to 2008 were considered. Studies with at least two years follow-up were eligible for analysis. The qualities of studies were assessed independently by two reviewers. In addition, to obtain a precise estimate, we used the sample size and the follow-up duration of each study as the covariates in the Bayesian meta-analysis model. The Winbugs and Boa softwares were utilized for fitting the Bayesian meta-analysis model.
Results: The estimate of relative risk of mortality for those who quit smoking compared to those continued smoking was 0.64 (95%CI: 0.57-0.70). We also did not find any significant relationship between the estimate of risk reduction and the described covariates.
Conclusions: Using this Bayesian meta-analysis, a 36% reduction in relative risk of mortality was found for those who quit smoking compared to those continued smoking, after eliminating the effects of study sample size and follow-up duration.

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Background and objective: Disease or mortality mapping are statistical methods aimed at providing precise estimates of rates across geographical maps. The aim of this research is to improve the precision of relative risk (RR) estimates of infant mortality (IM) for different rural areas, using empirical and full Bayesian methods.
Methods: Infant mortality data were extracted from the vital horoscope (Zij-Hayati) for years 2001 and 2006 across rural areas of Iran. Maximum Likelihood, Empirical Bayes with Poisson-Gamma model and full Bayesian models were used. Mont Carlo Markov Chain method was used for latter models. Deviance information criterion (DIC) was computed to check the models fittings. R, WinBUGS and Arc GIS software were employed.
Results: Based on the full Bayesian method, the highest RR of infant mortality was 1.73 (95%CI: 1.58-1.88) in year 2001 and 1.62 (95%CI: 1.50-1.75) in 2006 which belonged to Sistan-va-Blouchestan area in comparison to the whole country. In 2001, the rural areas of Birjand (1.45), Kordistan (1.23) and Khorasan (1.21) and in 2006, Birjand (1.42), Zanjan (1.39), Kordistan (1.36), Ardebil (1.32), Zabol (1.28), West Azerbaijan (1.18) and finally Golestan (1.14) had significant RR of IM (all p<0.05). The lowest RR of infant mortality for year 2001 were belong to rural areas of Tehran University (0.56) and for year 2006 to former Iran University (0.52).
Conclusion: To estimate the mortality map parameters, the full Bayesian method is preferred compared to empirical Bayes and maximum likelihood.

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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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Normal 0 false false false EN-US X-NONE AR-SA Background & Objectives: Cardiovascular disease is the first cause of death and third place in disease burden in Iran. This study estimates population attributable fraction (PAF) of cardiovascular risk factors in Tehran population. PAF is one of the important parameters of measuring population affect of risk factors and evaluating potential impact of preventive strategies in community level.
Methods: In this study 5868 participants above 30 years old of Tehran lipid and glucose study (TLGS) were employed and 501 CVD events detected during 10 years follow-up. Direct estimate of adjusted PAFs using logistic regression which is one of less biased exist methods of PAF calculation were applied.
Results: Highest modifiable cardiovascular risk factor PAFs, in sequence, was smoking (14.16%), hypertension (11.73%), diabetes (7.32%), hypercholesterolemia (6.85%) and central obesity (5.91%) for men, and hypertension (19.25%), diabetes (18.82%), central obesity(9.88%) and hypercholesterolemia (7.95%), for women. Also PAF of hazardous age and premature family history of CVD, as most important nonmodifiable CVD risk factors, were 36.09%, 16.61% and 3.95%, 7.56% for men and women respectively.
Conclusion: According to the difference of risk factors PAFs ranking in men and women, it is suitable that CVD preventive interventions to be prioritized by sex separately. In this regard, besides special attention to control tobacco use in men, hypertension and diabetes in both sexes and high cholesterol in men and central obesity in women respectively, should be given in priority of preventive strategies.

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Background & Objectives: Studying several linked markers provides more information on locating disease genes locus by using genetic association analysis.  The aims of this study were to introduce Multimarker Family Base Association Tests (FBAT-MM) and its Linear Combination (FBAT-LC) in multimarker genetic association analysis and to examine the association of selected microsatellites with HDL-C in an Iranian population.
Methods: One hundred twenty five (125) families having at least one member with metabolic syndrome and at least two members with low HDL-C were selected from participants of the Tehran Lipid and Glucose Study (TLGS). Multimarker genetic association of HDL-C level with some microsatellites in the chromosomes 8, 11, 12, and 16 were examined using FBAT-MM and FBAT-LC methods.
Results: The families consisted of 563 individuals (269 males and 294 females). FBAT-MM showed significant genetic association only between HDL-C and three microsatellites in Chromosome 11 (P<0.05). The microsatellite D11S1304 was found as the significant factor for multimarker genetic association.
Conclusion: FBAT-MM and FBAT-LC did not show shortcomings such as excessive conservatism and low power which are, usually, observed in other multimarker methods.  Finding microsatellites associated with HDL-C level can provide background for further researches on the role of predisposing genes in metabolic syndrome.

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Background and Objectives: Due to the importance of mortality statistics for planning, setting priorities and equal allocation of health services in population it is essential to assess quality of reporting mortality data in health systems. The aim of this study was to evaluate the completeness and accuracy of the Iranian Vital Horoscope reports for maternal and the under-five mortality (U5M) in rural areas through its comparison with other data sources in Iran.
Methods: The mortality data of Vital Horoscope reported from 30 selected cities over country was compared with the related data obtained from other data sources including Vital Horoscope's Fieldwork reports, Death Registration System and Maternal Mortality Surveillance System of Ministry of Health and Medical Education.
Results: Overall completeness of Vital Horoscope's Fieldwork reports for U5M in rural areas was about % 62.1. In terms of cause of death in children under-five,estimated sensitivity values were % 47.2 (95% CI: 22.9-72.2), % 66.6(95% CI: 22.7-95.7),  %78.2 (95% CI: 64.3-89.3)for respiratory infections, diarrhea and vomiting, and injuries-burning and poisoning respectively. The vital horoscope reports had 12.5% misclassification in determining the cause of maternal death.
Conclusion: Our findings indicate the Vital Horoscope's data might need some corrections because of underestimating of the mortality indicators. The comparison of this source with Death Registration System report for causes of death in children under-five (reported by Vital Horoscope) suggests that the vital horoscope might have suboptimal quality.

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Background and Objectives: Due to the importance of mortality statistics for planning, setting priorities and equal allocation of health services in population it is essential to assess quality of reporting mortality data in health systems. The aim of this study was to evaluate the completeness and accuracy of the Iranian Vital Horoscope reports for maternal and the under-five mortality (U5M) in rural areas through its comparison with other data sources in Iran.

 Methods: The mortality data of Vital Horoscope reported from 30 selected cities over country was compared with the related data obtained from other data sources including Vital Horoscope's Fieldwork reports, Death Registration System and Maternal Mortality Surveillance System of Ministry of Health and Medical Education.

 Results: Overall completeness of Vital Horoscope's Fieldwork reports for U5M in rural areas was about % 62.1. In terms of cause of death in children under-five,estimated sensitivity values were % 47.2 (95% CI: 22.9-72.2), % 66.6(95% CI: 22.7-95.7), %78.2 (95% CI: 64.3-89.3)for respiratory infections, diarrhea and vomiting, and injuries-burning and poisoning respectively. The vital horoscope reports had 12.5% misclassification in determining the cause of maternal death.

 Conclusion: Our findings indicate the Vital Horoscope's data might need some corrections because of underestimating of the mortality indicators. The comparison of this source with Death Registration System report for causes of death in children under-five (reported by Vital Horoscope) suggests that the vital horoscope might have suboptimal quality.

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Background & Objectives: Knowledge of the presence of seasonal trends and other explainable patterns in the prediagnostic data sources and removing such patterns before applying outbreak detection methods seem very important. This study aimed to detect and remove the explainable patterns such as seasonality, day-of-week (DOW) and holiday effects of the daily counts of suspected cases of measles in Iran.Methods: Data on daily counts of suspected cases of measles as a pre-diagnostic data source were obtained from Iranian national surveillance system between 21 March 2008 and 20 March 2011. We used lines plot, moving average chart, autocorrelation and partial autocorrelation functions for detecting explainable patterns. Moving average (MA) and Holt- Winters (HW) exponential smoothing method are used for removing explainable patterns.

Results: Our findings indicate the presence of seasonality, DOW effect, holidays and weekend effects in the daily counts of suspected cases of measles. The good performance of HW exponential smoothing technique in removing seasonal patterns is evident. MA technique showed better performance regarding assumption violation on outbreak detection methods.

 Conclusion: Because of the presence of explainable patterns in the daily counts of suspected cases of measles, considering such patterns before applying outbreak detection algorithms is very important. Implementing both MA (7 days) techniques for its simplicity as a pre- processing method and HW method for its efficacy in removing seasonal patterns is recommended.

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Background & Objectives: Evaluating the performance of outbreak detection methods using real data testing provide the highest degree of validity. The aim of this study was to determine the performance of the Exponentially Weighted Moving Average (EWMA) in real time detection of two local outbreaks in Iran.

Methods: The EWMA algorithm (both ƛ= 0.3 and 0.6) applied on daily counts of suspected cases of measles to detect local outbreaks which had been occurred in Mashhad and Bandar Abbas cities during 2010. The performance of The EWMA algorithms were evaluated using real data testing approach and reported by correlation analysis.

 Results: Mashhad outbreak was detected with a delay of about 2 to 7 days using EWMA algorithms as outbreak detection method while the utility of EWMA algorithms in real time detection of Bandar Abbas’ outbreak were on time good optimal. Maximum correlation value for EWMA 2 in relation to Mashhad outbreak was 0.60 at lag 2.

Conclusion: Applying the EWMA algorithm as an outbreak detection method at local levels is not suggested. However the characteristics of data are determinant of the performance of such detection methods.

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Background & Objectives: One of the most common anxiety disorders in children is separation anxiety disorder (SAD). The purpose of the present study was to investigate the epidemiology of separation anxiety disorder in Isfahan primary school male student.
Methods: The present study was a descriptive study. The statistical population was all the male students from 7 to 9 years old, studying in second and third grades in primary schools of Isfahan. The sample of study consisted of 1514 male student, who were selected randomly by multi-stage sampling method. The instruments of this study consisted of the Separation Anxiety Disorder Scale, Spence Children's Anxiety Scale and Clinical Interview. Data obtained were analyzed by using descriptive statistics and chi-square and logistic regression.
Results: The findings showed that the rate of separation anxiety disorder in male (7 to 9 year- old student) was %6/93. SAD were more common in 7 years old (7.4%). Children having record of hospitalization, immigration, single-parent households and close relatives of mortality had a higher rate of separation anxiety disorder.
Conclusion: This study shows that separation anxiety disorder among primary school male students is significantly high. Therefore it is important to consider the potential utility of early anxiety prevention/intervention programs, especially for children at this age group.

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Background & Objectives: Timely response to emerging diseases and outbreaks are a major public health and health systems priority. There are few published studies that evaluate the performance of cumulative sum (CUSUM) on identical data using semi- synthetic simulation approach. This study was undertaken to determine the performance of the CUSUM in timely detection of 831 days of simulated outbreaks.
Methods: We evaluated the performances of the CUSUM as an outbreak detection method on simulated outbreaks injected to daily counts of suspected cases of measles as baseline data in Iran between 21 March 2008 till 20 March 2011. Data obtained from the Iranian national surveillance system. The performance of algorithms was evaluated using sensitivity, false alarm rate, likelihood ratios and Area under the Receiver Operating Characteristic (ROC) curve.
Results: Generally the sensitivity of the CUSUM algorithm in detecting simulated outbreaks was 50% (95% CI: 47- 54). The corresponding values are disaggregated according to outbreak size, shape and duration. The CUSUM algorithm detected the half of outbreaks after 13.84 days on average.
 Conclusion: We concluded that CUSUM algorithm performed good in detection of large outbreaks with short periods and poorly in detecting long period outbreaks, particularly those simulated outbreaks that did not begin with a surge of cases.

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Background & Objectives: We studied the risk of Chronic Kidney Disease (CKD), Metabolic Syndrome (MetS), and their interaction on the incidence of Coronary Heart Disease (CHD).
Methods: A population of 6568 participants (43.4% male) with a mean age of 48.4 years for males and 46.7 years for females and a median follow-up of 10.1 years was investigated. They were divided into 4 groups at baseline: CKD-/MetS-, CKD+/MetS-, CKD-/MetS+, CKD+/MetS+. Hazard Ratios (HRs) were calculated for each group and were compared to the first group using multivariate Cox regression analysis adjusted for age, education, smoking, total cholesterol, and the family history of cardiovascular diseases.
 Results: Men with CKD (without MetS) showed an HR of 1.74 (CI 95%: 1.16-2.60) for CHD events. The measured value was 2.34 (1.77-3.08) for men with MetS (without CKD). The respective results were in women 1.18 (0.64-2.19) and 2.59 (1.73-3.88). CKD and MetS had a significant negative interaction with CHD events (HR=0.40, 0.24-0.66). The interaction was not significant in women (P value=0.48).
Conclusion: The results of this study indicated that CKD without MetS was a risk factor for coronary heart disease in men but not in women.

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  Background and Objectives : Various studies have reported different survival rates of patients with gastric cancer in Iran, and there is no overall estimate of the survival rate. The aim of this study was to conduct a meta-analysis of one, three, and five-year survival rate of patients with gastric cancer in Iran.

  Methods: In this study, all of the national databases including Iran Medex, Magiran, SID, and Medlib and the English databases including Google Scholar and PubMed were searched by using the keywords “stomach cancer”, “survival rate” and other Persian and English synonymous keywords, in the period 1392-1339 . Then, all articles with inclusion criteria and acceptable quality were investigated. Der Simonian and Laird random effects models were used to combine the results of all studies. Other analyses including subgroup analysis, sensitivity analysis, and assessment of publication bias were performed by using the funnel plot, and Beg’s and Egger’s tests. Finally, the data was analyzed using STATA software.

  Results: Of the 235 articles found in the initial search, nine studies were eligible for this study. According to these studies, one, three and five-years survival rate of patients with gastric cancer was 0.57 (95% CI: 0.45-0.70), 0.29 (95% CI: 0.22-0.37), 0.17 (95% CI: 0.13-0.21), respectively.

  Conclusion : Researches conducted in different parts of Iran are limited and there are no exact statistics on the survival rate in other parts of Iran. Therefore, further studies in the whole country are required to obtain more precise estimates of the survival and factors affecting it.

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Background & Objectives: Some studies on chronic diseases have indicated that obesity may result in a paradoxically longer survival. The present study was aimed to investigate the effect of Chronic Kidney Diseases (CKD) and Metabolic Syndrome (MetS) on the incidence of Coronary Heart Disease (CHD).

Methods: In order to record time to CHD events, a sample of 6,507 individuals (mean age 47.4 years, 43.4% males) was followed for nearly 10 years. Participants were also categorized into four groups according to presence/absence of CKD and MetS. Then, using a Multivariate Cox Regression, the Hazard Ratio (HR) of each group was estimated relative to individuals free of both CKD and MetS separately for obese (BMI&ge27.1 Kg/m2) and non-obese persons (BMI<27.1 Kg/m2).

Results: HR for non-obese patients with CKD but not MetS was obtained 2.06 (95%CI: 1.28-3.31) in men and 2.56 (1.04-6.31) in women. However, these associations were not significant for obese patients. Furthermore, among non-obese men and women with MetS alone, HR was estimated 2.52 (1.71-3.73) and 4.68 (2.20-9.95), respectively. For obese patients, these values were 1.70 (1.05-2.78) and 1.90 (1.16-3.13), respectively.

Conclusion: The results reflect that among those who had MetS alone, the risk of CHD incidence was twice higher in non-obese individuals compared to obese patients.

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In epidemiologic studies, the measurement of characteristics of interest is almost always subject to random measurement error. This error and its effects are usually overlooked by researchers. One of its effects is a widespread statistical phenomenon that is well known as regression to the mean. This phenomenon occurs whenever an extreme group of people is selected from a population based on their measurements of a variable. If a second measurement is taken in this group, the mean of the second measurement will be closer to the mean of the population.  In interventional studies, this increase (decrease) might be regarded as the effect of intervention, when in fact it has had no effect. Ignoring regression to the mean will lead to the erroneous conclusions and interpretation of the results of epidemiologic studies and affects the decisions in evidence-based medicine and planning for preventive and public health measures. This paper highlights the importance of this problem and its effects in epidemiologic studies and the ways to avoid it.

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Background and Objectives: Determining and monitoring the age at myocardial infarction (MI) and its comparison in various regions is one of the vital and basic principles for the management of MI.This study was conducted to determine the age at the first MI in Iran.

Methods: In this cross-sectional, analytical study at a national level, the data of 20750 new MI patients registered in 2012 in 31 provinces of Iran according to the cardiologist’s diagnosis, World Health Organization criteria, and the codes ICD10: I21-I22 were used. The calculations were done using the Stata 12 software.

Results: The mean±SD of age at the first MI was 61.2±13.4 years in all patients in Iran. Moreover, 15033 (72.4%) patients were male with a mean age of 59.6±13.3 years. The mean age at MI in men was lower than women (65.4±12.6 years), showing a statistical significance (P<0.001).The mean age at MI was different among Iran provinces, with a statistical significance (P<0.001). The lowest mean age at the first MI was seen in the patients living in Semnan (59.1±12.9 years), followed by Tehran (60.4±13.5 years), Sistan va Balouchestan (60.3±13.9 years), and Lorestan (60.1±14.5 years), with a significant difference from the mean age of the patients in Ardabil (63.2±15.1 years) and Zanjan (64.4±112.8 years) (P<0.019).

Conclusion: Our study demonstrated that the age at MI was approximately five years younger in men than women. This studyprovided the ground for monitoring the age at MI, and more effective management of cardiovascular diseases in Iran.