Validity and Reliability of the Human Immunodeficiency Virus-Related Stigma Questionnaire in Persian.

Background: It was aimed to adapt a 12-item questionnaire into Persian among people living with human immunodeficiency virus (PLHIV) in Markazi province. Material and Methods: Content validity was evaluated based on the opinions of the relevant experts, and by calculating the scale-level content validity index (S-CVI) and the item-level content validity index (I-CVI). Reliability was assessed via test-retest, intraclass correlation coefficient (ICC), and Cronbach's alpha. Results: The obtained scores on clarity and relevancy (I-CVI) ranged from 0.9 to 1. The S-CVI also had an acceptable validity of 0.99. The Cronbach's alpha index of the whole questionnaire was 0.84 and ranged from 0.69 to 0.82 for subscales. The ICC in test-retest for all questionnaires was 0.88 and for subscales ranged from 0.77 to 0.88. Conclusion: The Persian version of the 12-item human immunodeficiency virus-related stigma questionnaire was found to be, in addition to being short and comprehensive, acceptable reliability and high validity to use in order to determine the stigma related to Persian-speaker PLHIV.

A framework for exploration and cleaning of environmental data--Tehran air quality data experience.

BACKGROUND: Management and cleaning of large environmental monitored data sets is a specific challenge. In this article, the authors present a novel framework for exploring and cleaning large datasets. As a case study, we applied the method on air quality data of Tehran, Iran from 1996 to 2013.  METHODS: The framework consists of data acquisition [here, data of particulate matter with aerodynamic diameter ≤10 µm (PM10)], development of databases, initial descriptive analyses, removing inconsistent data with plausibility range, and detection of missing pattern. Additionally, we developed a novel tool entitled spatiotemporal screening tool (SST), which considers both spatial and temporal nature of data in process of outlier detection. We also evaluated the effect of dust storm in outlier detection phase. RESULTS: The raw mean concentration of PM10 before implementation of algorithms was 88.96 µg/m3 for 1996-2013 in Tehran. After implementing the algorithms, in total, 5.7% of data points were recognized as unacceptable outliers, from which 69% data points were detected by SST and 1% data points were detected via dust storm algorithm. In addition, 29% of unacceptable outlier values were not in the PR.  The mean concentration of PM10 after implementation of algorithms was 88.41 µg/m3. However, the standard deviation was significantly decreased from 90.86 µg/m3 to 61.64 µg/m3 after implementation of the algorithms. There was no distinguishable significant pattern according to hour, day, month, and year in missing data. CONCLUSION: We developed a novel framework for cleaning of large environmental monitored data, which can identify hidden patterns. We also presented a complete picture of PM10 from 1996 to 2013 in Tehran. Finally, we propose implementation of our framework on large spatiotemporal databases, especially in developing countries.

Comparison of two data mining techniques in labeling diagnosis to Iranian pharmacy claim dataset: artificial neural network (ANN) versus decision tree model.

BACKGROUND: This study aimed to evaluate and compare the prediction accuracy of two data mining techniques, including decision tree and neural network models in labeling diagnosis to gastrointestinal prescriptions in Iran. METHODS: This study was conducted in three phases: data preparation, training phase, and testing phase. A sample from a database consisting of 23 million pharmacy insurance claim records, from 2004 to 2011 was used, in which a total of 330 prescriptions were assessed and used to train and test the models simultaneously. In the training phase, the selected prescriptions were assessed by both a physician and a pharmacist separately and assigned a diagnosis. To test the performance of each model, a k-fold stratified cross validation was conducted in addition to measuring their sensitivity and specificity. RESULT: Generally, two methods had very similar accuracies. Considering the weighted average of true positive rate (sensitivity) and true negative rate (specificity), the decision tree had slightly higher accuracy in its ability for correct classification (83.3% and 96% versus 80.3% and 95.1%, respectively). However, when the weighted average of ROC area (AUC between each class and all other classes) was measured, the ANN displayed higher accuracies in predicting the diagnosis (93.8% compared with 90.6%). CONCLUSION: According to the result of this study, artificial neural network and decision tree model represent similar accuracy in labeling diagnosis to GI prescription.

Serum HDL-C level of Iranian adults: results from sixth national Surveillance of Risk Factors of Non-Communicable Disease.

BACKGROUND: Reduced level of high-density lipoprotein-cholesterol (HDL-C) is shown to be in association with the risk of coronary artery disease (CAD), metabolic syndrome, and chronic renal disease. Lack of a national representative research for assessing the level of HDL-C among Iranian adults, which is essential for health policy makers, was the motivation for this study. METHODS: HDL-C levels of 4,803 Iranian adults aged 25-64 years old were measured by sixth national Surveillance of Risk Factors of Non-Communicable Disease (SuRFNCD) in 2011. Data were entered into STATA 12 software and were analyzed using fractional polynomial model and other statistical methods. RESULTS: In average, Iranian adult women had 5.8 ± 0.3 mg/dL higher HDL-C level than men. The analysis showed that the HDL-C levels will be changed at most 3 mg/dL from the age of 25 to 64 years. Furthermore, it was shown that approximately half of the men and one third of the women had HDL-C level less than 40 mg/DL. Also HDL-C level of more than 60% of the women was less than 50 mg/dL. CONCLUSIONS: High level of HDL-C among Iranian adults was shown in this study which can be a major reason of increasing incidence of heart diseases in Iran. Hence, formulating policy regulations and interventions in Iranian lifestyle to reduce HDL-C levels should be among top priorities for health politicians.

Application of Gaussian Process Regression (GPR) in estimating under-five mortality levels and trends in Iran 1990 - 2013, study protocol.

BACKGROUND: Searching for the latest methods of estimating mortality rates is a major concern for researchers who are working in burden of diseases. Child mortality is an important indicator for assessing population health care services in a country. The National and Sub-national Burden of Diseases, Injuries, and Risk Factors (NASBOD) is conducted in Iran with comparative methods and definitions of Global Burden of Disease (GBD) 2010 to estimate major population health measures including child mortality rate. The need to have accurate and valid estimation of under-5 mortality rate led to apply more powerful and reliable methods. METHOD: The available datasets consist of under-five mortality rates from different sources including death registration systems and summary birth history (SBH) questions from censuses and Demographic Health Survey. These datasets are gathered at national and sub-national levels. We have five time series of under-five mortality rates from SBH method that each one contains 25-year time period. We also calculated Child mortality rates from death registration for 5 years. The main challenge is how to combine and integrate these different time series and how to produce unified estimates of child mortality rates during the course of study. By synthesizing the result of other models, Gaussian Process Regression (GPR) is used as the final stage for generating yearly child mortality rates in this study. GPR is a Bayesian technique that uses data information and defines several hierarchical prior parameters for model. In corporation of GPR and MCMC methods, predicted rates are updated using data and defined parameters in model. This method, also captures both sampling and non-sampling errors and provides uncertainty intervals. The existence of uncertainty for predicting mortality rate is one of the considerable advantages of GPR that distinguish it from other alternative methods. DISCUSSION: Estimating accurate and reliable child mortality rates at national and sub-national levels is one of the important parts of NASBOD project in Iran. Gaussian Process Regression with its special features improves achievement of this goal. GPR is a serious competitor for other supervised mortality predictive methods. This article aims to explain the application and preferences of GPR method in estimating child mortality rate.

Application of spatio-temporal model to estimate burden of diseases, injuries and risk factors in Iran 1990 - 2013.

BACKGROUND: Identifying the burden of disease and its inequality between geographical regions is an important issue to study health priorities. Estimating burden of diseases using statistical models is inevitable especially in the context of rare data availability. To this purpose, the spatio-temporal model can provide a statistically sound approach for explaining the response variable observed over a region and various times. However, there are some methodological challenges in analysis of these complex data. Our primary objective is to provide some remedies to overcome these challenges. METHOD: Data from nationally representative surveys and systematic reviews have been gathered across contiguous areal units over a period of more than 20 years (1990 - 2013). Generally, observations of areal units are spatially and temporally correlated in such a way that observations closer in space and time tend to be more correlated than observations farther away. It is critical to determine the correlation structure in space-time process which has been observed over a set of irregular regions. Moreover, these data sets are subject to high percentage of missing, including misaligned areal units, areas with small sample size, and may have nonlinear trends over space and time. Furthermore, the Gaussian assumption might be overly restrictive to represent the data. In this setting, the traditional statistical techniques are not appropriate and more flexible and comprehensive methodology is required. Particularly, we focus on approaches that allow extending spatio-temporal models proposed previously in the literature.Since statistical models include both continuous and categorical outcomes, we assume a latent variable framework for describing the underlying structure in mixed outcomes and use a conditionally autoregressive (CAR) prior for the random effects.  In addition, we will employ misalignment modeling to combine incompatible areal units between data sources and/or over the years to obtain a unified clear picture of population health status over this period.  In order to take parameter uncertainties into account, we pursue a Bayesian sampling-based inference. Hence, a hierarchical Bayes approach is constructed to model the data. The hierarchical structure enables us to "borrow information" from neighboring areal units to improve estimates for areas with missing values and small number of observations. For their general applicability and ease of implementation, the MCMC methods are the most adapted tool to perform Bayesian inference. CONCLUSION: This study aims to combine different available data sources and produce precise and reliable evidences for Iranian burden of diseases and risk factors and their disparities among geographical regions over time. Providing appropriate statistical methods and models for analyzing the data is undoubtedly crucial to circumvent the problems and obtain satisfactory estimates of model parameters and reach accurate assessment.

Weight, height and body mass index nomograms; early adiposity rebound in a sample of children in tehran, iran.

BACKGROUND: Assessing growth is a useful tool for defining health and nutritional status of children. The objective of this study was to construct growth reference curves of Iranian infants and children (0-6 years old) and compare them with previous and international references. METHODS: Weight, height or length of 2107 Iranian infants and children aged 0-6 years old were measured using a cross-sectional survey in Tehran in 2010. Standard smooth reference curves for Iranian population were constructed and compared to multinational World Health Organization 2006 reference standards as well as a previous study from two decades ago. RESULTS: Growth index references for Iranian girls are increased in compare to data from two decades ago and are approximately close to the international references. In boys; however, the increment was considerably large as it passed the international references. Not only the values for indexes was changed during two decades, but also the age at adiposity rebound came near the age of 3, which is an important risk factor for later obesity. CONCLUSIONS: During two decades, growth indexes of Iranian children raised noticeable. Risk factors for later obesity are now apparent and demand immediate policy formulations. In addition, reference curves presented in this paper can be used as a diagnostic tool for monitoring growth of Iranian children.

Knowledge, Attitude and Practice of Tehran's Inhabitants for an Earthquake and Related Determinants.

UNLABELLED: Background A major destructive earthquake is predicted to shake the Tehran city in the near future. To mitigate the damage from such earthquakes, it is necessary to assess the preparedness of people and find the related risk factors. Methods A cross-sectional study was conducted in Tehran city among people aged 15 years or older in 2009. 1195 of Tehran's residents were interviewed using a questionnaire. Pearson chi-square test and binary logistic regression were used in order to evaluate the factors associated with preparedness against an earthquake. Results The analysis showed that 1076 (90.0%), 1160 (97.1%), and 490 (41.0%) of the participants achieved half of the possible scores for the knowledge, attitude, and practice components, respectively. Furthermore, in multivariate analysis low knowledge (p<0.001), having a high-school (p=0.033) or lower education (p<0.001) and living in Northern high-risk regions (p<0.001) of the Tehran were identified as risk factors for taking precautionary measures against earthquake. For low knowledge, lack of previous experience (p<0.001), and working as labor, businessman, employee (p=0.001) or being housewife (p=0.002) were related risk factors. In addition, people in the Southern high risk regions were significantly more knowledgeable (OR=0.618 compared to people in low risk regions) about earthquakes. Conclusions It is suggested that preparedness programs should target people with lower educational level and people in high risk regions especially the Northern districts of the city and aim at increasing public knowledge about earthquakes. Address for correspondence: Ali Ardalan, No. 78, Italia Ave, Department of Disaster Public Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. Email: aardalan@gmail.com or aardalan@tums.ac.ir CITATION: Ostad Taghizadeh A, Hosseini M, Navidi I, Mahaki AA, Ammari H, Ardalan A. Knowledge, Attitude and Practice of Tehran's Inhabitants for an Earthquake and Related Determinants. PLOS Currents Disasters. 2012 Aug 6.