Iranian Journal of Epidemiology
Year:2015 | Volume:11 | Issue:3|Papers Count:11

Background and Objectives: In this regard, in addition to reducing exposure, the most effective way to prevent hepatitis B is vaccination. The coverage of hepatitis B vaccination in the Iranian Health Personnel is reportedly different in various studies and there is not an overall estimate of it. Therefore, the present study was conducted to determine the immune response to hepatitis B vaccine among Iranian Health Personnel using the meta-analysis method.Methods: This study was a systematic review of the Iranian databases, including Magiran, Iran Medex, IRANDOC, SID, MEDLIB and international databases, including Scopus, PubMed, ScienceDirect and the Google Scholar search engine without any time limit to 2015 using standard Persian and English keywords. Searches were conducted by two researchers independently. Meta-analysis was performed using STATA, version 11.1 to combine data through Random Effects Model.Results: We evaluated 6311 subjects in 21 studies. The history of injection and complete coverage of hepatitis B vaccination in the Health Personnel was 86.9% (CI: 95%, 83-90.7) and 70.3% (CI: 95%, 65.6-75), respectively.The minimum and maximum vaccination coverage were related to the North (52.7%) and West of Iran (87.2%), respectively.Conclusion: The coverage of hepatitis B vaccination in Iranian Health Personnel is less than European countries, which requires new management strategies and policies to maximize hepatitis B vaccination in high risk professional groups.

Background and Objectives: completeness of registration is used as one of the measures of the quality of a cancer registry, which is the degree to which reportable incident cases of cancer in the population of interest is actually recorded in the registry.Methods: After removing the duplicates, a total of 471 new cases of esophagus cancer reported by three sources of pathology reports, medical records, and death certificates to Ardabil Province Cancer Registry Center in 2006 and 2008 were enrolled in the study. The incidence rate was estimated based on the capture-recapture method and the use of the log-linear models. BIC, G2 and Akaike statistics were used to select the best-fit model.Results: In this study, a model with linkage between pathology reports and medical records and a model with death certificates alone, independent of the previous two sources, was the best fitted model. The estimated total completeness of esophagus cancer in 2006 and 2008 was 36%.The source that had the most completeness for esophagus cancers was pathology reports with 21.17%. The estimated incidence rate calculated by the loglinear method for the years 2006 and 2008 was 49.71 and 53.87 per 100, 000 population, respectively.Conclusion: Based on the obtained results, it can be concluded that the low degree of completeness in Ardabil Province requires some changes in data abstracting and case finding such as the use of personal national code and electronic health records to create a more accurate cancer registry.

Background and Objectives: Breast cancer is one of the most common cancers among women and is the second main cause of death after lung cancer. The objective of this study was to use the Bayes model to analyze the prognostic effects on the survival of the women with breast cancer after surgery in the south of Iran.Methods: The date was collected 1192 women who had breast cancer in Namazi Hospital Research Center between 2001 and 2006. The complete information of only 1148 of them was registered. Parametric Bayes and Bayes Cox methods were used. Considering 0.05 as the level of significance, the data analysis was done using the WinBUGS14 software.Results: The mean age of the patients (at the time of diagnosis) was 47 years in this study. Cox one-variable analysis showed a significant relationship between survival and smoking (P=0.009), bone metastasis (P=0.01), the number of lymph nodes (P=0.001), the tumoral level of malignancy (P=0.001), the surgical method (P=0.015), financial status (P=0.025), and the tumor size (P=0.001). By fitting Bayes models the variables tumor size, level of malignancy and number of lymph nodes were significant.Conclusion: The results showed that clinicopathological features of cancer had a significant role in the survival of the patients.

Background and Objectives: Food insecurity is a major public health problem. The impact of unplanned pregnancy on the physical and mental health of the mother and the fetus is unfavorable. This study was conducted to compare the relationship between food insecurity and some demographic and socioeconomic characteristics, fertility, and pregnancy in women with planned and unplanned pregnancy in Falavarjan in 1393 (2014).Methods: This case-control study was performed on 200 women with unplanned pregnancy (cases) and 200 women with planned pregnancy (controls) using systematic random selection. Information was collected through researcher-made and Food Security questionnaires (USDA) through interviews with the mothers. Data was analyzed with the SPSS software version 16.0 and chi-square, t-test, Mann-Whitney test, correlation coefficient, and logistic regression.Results: The prevalence of food insecurity was significantly higher in cases (51%) than in controls (37%). Food insecurity increased the risk of unplanned pregnancy by 1.15 times (CI 95% for OR 1.08-1.22). The risk of unplanned pregnancy, in terms of food insecurity without hunger was 0.324 times more than food security conditions. The odds increased in the state of food insecurity with hunger. In groups, demographic characteristics (except for the age of the household head in the control group), fertility and pregnancy had a significant direct relationship and socioeconomic status had a significant inverse relationship with the household food security.Conclusion: Food insecurity is associated with demographic and socioeconomic characteristics and the history of fertility and pregnancy. Unplanned pregnancy endangers the outcome of pregnancy and the health of the future generations.

Background and Objectives: Missing data is a big challenge in the research. According to the type of the study and of the variables, different ways have been proposed to work with these data. This study compared five popular imputation approaches in addressing missing data in the questionnaires.Methods: In this study, 500 questionnaires were used for self-medication in diabetic patients. Missing in the observations was artificially generated by random selection of questions and then deleting them. Five imputation ways included: 1) the mean of the questions, 2) the mean of the person, 3) the mode of the person, 4) linear regression, and 5) EM algorithm. For each method, the mean and standard deviation were compared with imputation. The Spearman correlation coefficient, the percentage of incorrectly classified and kappa statistic were also calculated.Results: A kappa higher than 0.81 represented almost perfect agreement at 10% missingness. The EM algorithm showed the highest level of agreement with the results of actual data with a Kappa of 0.886. With increasing missingness to 30%, the EM algorithm and the mean of the person showed a rather similar agreement with a Kappa of 0.697 and 0.687, respectively.Conclusion: In this study, the EM algorithm was the most accurate method for handling missing data in all models.The mean of the person method is easy for handling missing data, especially for most non statisticians.

Background and Objectives: Disorders of height and weight growth are the most important health disorders affecting children younger than two in developing societies. Failure to treat these disorders can lead to the increased mortality and mental, emotional or physical disability. The objective of this study was to investigate the growth trends of children and the factors affecting it.Methods: This retrospective cohort study was conducted on 2030 children younger than two in Khorramabad, Iran who were selected using stratified and cluster sampling. Based on household records, the weight growth trend was recorded as a four-level variable (decline, stagnation, slowness and desirable) and the the height growth trend was recorded as a three-level variable (stagnation, slowness and desirable); finally, the data were modeled using a longitudinal marginal model and the SAS software version 9.2.Results: The incidence of at least one decline in the weight growth curve and one stagnation in the height growth curve was 14.2% and 10.4%, respectively. The child’s age and the maternal educational level had a significant effect on the growth trends. However, the sex, parity and the exclusive breastfeeding during the first six months had no significant relationship with the growth trends.Conclusion: Given the results and the relatively high prevalence of growth disorders among children, it seems that increasing the awareness of low literate women about feeding of the children is the most important approach to manage growth disorders. Additionally, health-care professionals should mostly focus on monitoring the growth of children older than 12 months.

Background and Objectives: Diabetes is a chronic and common metabolic disease which has no curative treatment. Logistic regression (LR) is a statistical model for the analysis and prediction in multivariate statistical techniques. Discriminant analysis is a method for separating observations in terms of dependent variable levels which can allocate any new observation after making discriminating functions. The aim of this study was to compare and determine the effective variables in type 2 diabetes.Methods: The data included 5357 persons obtained through a cohort study in Kerman, southeastern Iran, in 2009-11. Diabetes was considered the response variable. The independent variables after deleting colinearity and correlated variables included height, waist circumference, age, gender, occupation, education, drugs, systolic blood pressure, HDL, LDL, drug abuse, activities, and triglyceride. Sensitivity, specificity, accuracy, and ROC curve were applied for determining and comparing the prediction power of the models.Results: The results in the reduced model with extracted significant variables from the full model, the sensitivity of the LR model and DA was 74% and 22.4%, the specificity of the LR model and DA was 71.1 % and 95.4 %, the prediction accuracy of the LR model and DA was 71.5% and 85.3%, and the ROC curve of the LR model and DA was 80.3% and 80.1%, respectively. Simulation showed the sensitivity, specificity, accuracy, and ROC curve was 99.18%, 98.49%, 98.59%, and 99.9% for the LR model and 92.62%, 99.19%, 98.26%, and 99.56% for DA, respectively.Conclusion: The results showed that the risk factors of diabetes in the logistic regression reduced model were waist circumference, age, gender, LDL level, systolic pressure, and drugs. Also, the sensitivity of the LR model was more than DA while DA had a higher specificity and prediction accuracy. Comparison of the ROC curve showed that the prediction estimated values were rather similar in both models, but the two models were the same asymptotically.

Background and Objectives: GTN is a general term for an extensive range of malignant trophoblastic diseases including invasive mole, choriocarcinoma, epithelioid trophoblastic tumors and placental site trophoblastic tumors.The aim of this study was to predict the risk of GTN in patients with molar pregnancy in Tehran.Methods: All cases with partial and complete mole with a record of at least 4 titers of b-hCG were included in this study. Before and after fitting the appropriate model for calculating the area under the curve of each predictor variable, the type of the relationship (linear or non-linear) was first determined using locally weighted scatter plot smoothing (Lowess Smoother) and fractional polynomial regression (Fracpoly); then, a model tailored to data processing was used for drawing the ROC diagram.Results: Nonparametric chi-square analysis indicated no significant difference between the components of highrisk molar pregnancy and GTN (P=0.39). Generally, among 201 cases of molar pregnancy, 61 (30%) had one of the components of high-risk molar pregnancy. The ROC curve with an AUC of 0.86 showed that the regression slope of b-hCG with 73% sensitivity and 88% specificity could be used as a predictor.Conclusion: The serum b-hCG measurement after 21 days of molar pregnancy evacuation and the slope of the linear regression line of b-hCG were found be good tests to distinguish between patients who will benefit from spontaneous disease remission and patients developing GTN.