Iranian Journal of Epidemiology
Year:2018 | Volume:14 | Issue:2|Papers Count:10

Background and Objectives: Life satisfaction is one of the important dimensions of health, which is influenced by health determinants. The aim of this study was to investigate the status of socioeconomic inequalities in satisfaction with life in women aged 15-54 in Iran. Methods: In this cross-sectional study, randomized multistage cluster sampling with equal clusters was done to select the participants. A total of 35, 305 women aged 15-55 were enrolled in the study. Data analysis was done in two stages. In the first stage, social and economic inequalities were investigated using the concentration index and concentration curve method. In the second stage, a multilevel method was used to identify the determinants. Results: The mean life satisfaction was 12. 81± 4. 23. The concentration index for dissatisfaction with life was-0. 06 [95% CI:-0. 1,-0. 02], indicating dissatisfaction with life in low socioeconomic groups. The results of multilevel analysis showed that age, marital status, occupation, place of living, education, and the economic class correlated with dissatisfaction with life in women (P<0. 05). Conclusion: There is inequality in dissatisfaction with life. Dissatisfaction is concentrated in the poorer groups of the society and varies in different provinces. Socioeconomic variables affect inequality in satisfaction with life in women, which need to be addressed to reduce inequalities.

Background and Objectives: The pandemic of HIV/AIDS is spreading rapidly in the Middle East, in particular in Iran. Designing an educational program involves assessment of the educational needs of people with valid and culture-based tools. This study was conducted with the aim of psychometric analysis of an instrument for assessment of HIV/AIDS knowledge based on the Iranian culture in different population groups. Methods: In this psychometric analysis, 58 items related to AIDS knowledge were prepared based on a literature review. Then, the face validity and content validity of the instrument was determent by the impact item score, content validity ratio (CVR), and content validity index (CVI). The reliability of the scale was assessed by Cronbach’ s alpha coefficient and test-retest. Results: Based on face validity and content validity, the research scale had 44 items with an item impact score > 1. 5, a CVR > 0. 49, and a CVI > 0. 79. The Scale had three sections, general knowledge about HIV/AIDS (9 items), knowledge about HIV transmission (25 items), and knowledge about HIV/AIDS prevention (10 items). The Scale also had a high reliability (Cronbach’ s alpha coefficient=0. 77-0. 91, ICC=0. 88) Conclusion: The results of the study showed the instrument was a valid and reliable scale for measurement of knowledge about HIV/AIDS.

Background and Objectives: Tuberculosis is a chronic bacterial disease and a major cause of morbidity and mortality. It is caused by a Mycobacterium tuberculosis. Awareness of the incidence and number of new cases of the disease is valuable information for revising the implemented programs and development indicators. time series and regression are commonly used models for prediction but these methods require some assumptions. The purpose of this study was to predict new TB cases using the hidden Markov model which does not require many assumption. Methods: The data used in this study was the monthly number of new TB cases during 2006-2016 identified and recorded in Hamedan Province. Rorecasting the number of new TB cases was done using hidden Markov models using the hidden Markov package in the R software. Results: According to the AIC and BIC criterion, two states had the best fit to the data, i. e. the data of this study were a mixture of two Poisson distributions with average number of event 5. 96 and 10. 2 respectively. The results also predicted the number of new cases over the next 24 months based on the hidden Markov model would be between 8 and 9 new cases in each month. Conclusion: The hidden Markov model is the best model for prediction using the Markov chain. This model, in addition to detection of an appropriate model for the available data, can determine the transition probability matrix, which can help physicians predict the future state of the disease and take preventive measures befor reaching advanced stages.

Background and Objectives: Evidence suggests that underlying diseases increase the severity of influenza and lead to hospitalization or death. This study was conducted to determine the risk factors associated with hospitalization of patients in Afzalipour Hospital, Kerman, Iran during an outbreak of H1N1 influenza in December 2015. Methods: In this case-control study, the case group comprised 85 patients who were hospitalized for influenza and the control group included 51 patients who had influenza symptoms and were discharged after required evaluations and check-up. The data were collected from both groups on a daily basis for two weeks. For data analysis, descriptive analysis, logistic regression analysis, Lasso Regression, and likelihood ratio were used. Analysis was performed using the Stata version 12 and R software. Results: Among the variables examined, after removal of additional variables, 12 variables were introduced into the multivariate regression. The history of pulmonary disease and diabetes increased the odds of hospitalization following influenza by more than 11 (OR = 11. 6, P. value = 0. 003) and 9 times (OR = 9, P. value = 0. 01), respectively. Conclusion: Underlying disease and factors play a major role in exacerbating the disease. Therefore, the health system should take the necessary preventive measures when outbreaks occur.

Background and Objectives: The cause of death in children under one year can be an important tool for designing prevention strategies and reducing the mortality rate. The aim of this study was to estimate the number of deaths in children under one year using the Mr. Murray’ s estimation index in Mazandaran Province, and to compare this estimation with reported cases of civil registration organization. Methods: All deaths of children under one year between 2011 to 2014 registered in hospitals across Mazandaran Province were included in this study. The cause of death as coded in the International Classification of Diseases (ICD-10) was converted to Murray classification. The coefficients in each of the Murray levels were used to estimate actual death cases. We compared this estimation with the number of deaths that is reported annually by civil registration organization. Results: Seven hundred and sixty four deaths occurred in this period. The leading causes of death in children under one year were conditions of the perinatal period, congenital anomalies and chromosomal disorders, respiratory diseases, and diseases of the cardiovascular system. The Murray method estimated 1711 deaths for the entire Province. Conclusion: the Murray method predicted that from 2011 to 2014, 390-445 children under one year died in Mazandaran Province annually. There is a controversy between the estimates obtained in this study and the number of deaths reported by the civil registration organization, which may indicate a defect in a complete registration of deaths by this organization.

Background and Objectives: Identification of statistical models has a great impact on early and accurate detection of outbreaks of infectious diseases and timely warning in health surveillance. This study evaluated and compared the performance of the three data mining techniques in time series prediction of brucellosis. Methods: In this time series, the data of the human brucellosis cases and climatology parameters of Hamadan, west of Iran, were analyzed on a monthly basis from 2004 (March/April) to 2017 (February/March). The data were split into two subsets of train (80%) and test (20%). Three techniques, i. e. radial basis function (RBF) and multilayer perceptron (MLP) artificial neural network methods as well as K Nearest neighbor (KNN), were used in both subsets. The root mean square errors (RMSE), mean absolute errors (MAE), mean absolute relative errors (MARE), determination coefficient (R2) and intra-class correlation coefficient (ICC) were used for performance comparison. Results: Results indicated that RMSE (23. 79), MAE (20. 65) and MARE (0. 25) for MLP were smaller compared to the values of the other two models. The ICC (0. 75) and R2 (0. 61) values were also better for this model. Thus, the MLP model outperformed the other models in predicting the used data. The most important climatology variable was temperature. Conclusion: MLP can be effectively applied to diagnose the behavior of brucellosis over time. Further research is necessary to detect the most suitable method for predicting the trend of this disease.

Background and Objectives: Breast cancer is the most common cancer in Iran. It can be prevented by rapid diagnosis of the disease. Thus, it is necessary to determine the causal relationships between variables related to breast cancer. Bayesian network is a data mining tool that shows the causal relationship between different variables. In this paper, a Bayesian network was applied to find causal relationships between breast cancer variables using a genetic algorithm in a graphical model. Methods: in this applied study, data were collected from 900 breast cancer patients in Kerman Province from 1999 to 2008. For data analysis, we used a probabilistic graphical model representing the causal relationship between variables. Results: The results showed that surgery was the most important treatment for breast cancer. Based on the conditional and marginal probabilities, the women who underwent surgery had higher hopes of living longer. Moreover, 81% of the patients who did not undergo surgery only received chemotherapy or radiotherapy were less likely to have long lives. Conclusion: People aged 40-65 years are more likely to have breast cancer. Moreover, the variables of age, surgery, chemotherapy, and radiotherapy had a direct effect on the status of the patients and there were direct edges from these variables to the status of the patients.

Background and Objectives: Epidemics of diarrhea caused by contaminated food and water occur frequently worldwide, especially in developing countries. The aim of this study was to identify the factors contributing to the development and spread of diarrhea epidemics caused by Shigella flexneri in Isfahan in the spring of 2015. Methods: Following the first reports and activation of the surveillance system, a matched case-control study was conducted in 360 cases with diarrhea and 360 controls. Data were gathered using a questionnaire, and conditional logistic regression was used to provide adjusted odds ratios and 95% confidence intervals. Results: Consumption of contaminated fresh vegetable (OR=4. 50, 95% CI=2. 88-7. 01) was the source of the epidemic. Contact with similar patients (OR=7. 19, 95% CI=4. 26-12. 12), presence of similar patients in the family (OR=33. 2, 95% CI=13. 64-80. 82), food consumption on special events (party, wedding ceremony, funeral, etc. ) (OR=1. 67, 95% CI=1. 06-2. 62), being a government employee (OR=3. 08, 95% CI=1. 64-5. 78) and being illiterate (OR=2. 91, 95% CI=1. 10-7. 67) were the risk factors of the spread of the epidemic. Conclusion: In this study, consumption of fresh vegetable contaminated with Shigella flexneri was the source of the infection. According to the Epidemic Management Team, contamination occurred due to non-standard wastewater irrigation of agricultural lands. Therefore, it is important to consider the standards of agricultural production and compilation of a comprehensive program to improve the status of the wastewater purification system in the environmental sciences by the relevant organizations to prevent diarrhea epidemics.

Background and Objectives: Annually about 7 million premature deaths occur due to air pollution in the world. Nitrogen oxides are among major air pollutants. Although many foreign exposure assessment studies have been carried out, Iranian studies are limited to primary analyses. Hence, in this research, we studied spatial variation of nitrogen oxides using spatiotemporal modeling in Tehran 2014. Methods: The concentration of nitrogen oxides was obtained from 21 air pollution monitoring stations in Tehran. There were 8760 records for each pollutant in each station. Holidays and land elevation were the predictors implemented in the spatiotemporal model. The D-STEM software was used for analyses and mapping. Results: Nitrogen monoxide significantly decreased (P<0. 001) over holidays and with an increase in land elevation (coefficient:-0. 070 and-0. 169, respectively). Moreover, the concentration of nitrogen dioxides decreased in holidays (coefficient:-0. 630) but increased with with an increase in land elevation (coefficient: 0. 155) (P<0. 001). Conclusion: Spatiotemporal exposure assessment of nitrogen oxide pollutants was done for residents of Tehran for the first time in this study. The results of this study, which are estimation maps for daily nitrogen oxides, could benefit future epidemiological studies in evaluation of the effect of air pollutions on health of Tehran citizens.

Background and Objectives: Evaluation of the epidemiologic pattern of diseases requiring hospitalization is one of the effective factors in better management of hospitals, which can provide health managers and planners with a realistic and appropriate view on procuring the equipment and furnishing and equipping the hospitals in the country. This study was conducted to investigate this pattern based on the International Classification of Diseases (ICD-10) using the information recorded in Tehran Health Insurance Organization in a ten-year period. Methods: In this descriptive research, we examined all records of hospitalized cases registered in the hospital information system of Tehran Health Insurance Organization during 2005 to 2014. The data of more than two million records, including case number, gender, and age of the patients as well as the diagnosis code were extracted from the hospital information system. Results: From 2008859 cases, 51. 9% of patients were male and the mean age of the patients was 24. 60± 44. 22 years. In children less than one year, “ certain conditions originating in the perinatal period” , in children aged one to four years “ symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified” , in children 5 to 14 years “ neoplasm” , in patients 15 to 44 years “ pregnancy, childbirth and the puerperium” , and in patients above 45 years, “ diseases of the circulatory system” were the most common diagnostic categories in hospitalized insured patients. Conclusion: Due the specialization of diseases and various causes of hospitalization, to prevent wastage of financial and human resources, health policymakers and planners should allocate resources to insurance companies according to the scope of their activities.