Joint Disease Mapping of Two Digestive Cancers in Golestan Province, Iran Using a Shared Component Model

Objectives: Recent studies have suggested the occurrence patterns and related diet factor of esophagus cancer (EC) and gastric cancer (GC). Incidence of these cancers was mapped either in general and stratified by sex. The aim of this study was to model the geographical variation in incidence of these two related cancers jointly to explore the relative importance of an intended risk factor, diet low in fruit and vegetable intake, in Golestan, Iran. Methods: Data on the incidence of EC and GC between 2004 and 2008 were extracted from Golestan Research Center of Gastroenterology and Hepatology, Hamadan, Iran. These data were registered as new observations in 11 counties of the province yearly. The Bayesian shared component model was used to analyze the spatial variation of incidence rates jointly and in this study we analyzed the data using this model. Joint modeling improved the precision of estimations of underlying diseases pattern, and thus strengthened the relevant results. Results: From 2004 to 2008, the joint incidence rates of the two cancers studied were relatively high (0.8-1.2) in the Golestan area. The general map showed that the northern part of the province was at higher risk than the other parts. Thus the component representing diet low in fruit and vegetable intake had larger effect of EC and GC incidence rates in this part. This incidence risk pattern was retained for female but for male was a little different. Conclusion: Using a shared component model for joint modeling of incidence rates leads to more precise estimates, so the common risk factor, a diet low in fruit and vegetables, is important in this area and needs more attention in the allocation and delivery of public health policies. © 2015

Prevalence of depression among infertile couples in Iran: A meta-analysis study

Background: Several studies have been conducted in Iran in order to investigate the prevalence of depression among infertile couples. However, there is a remarkable diversity among the results. This meta-analysis was conducted to estimate an overall prevalence rate of depression among infertile couples in Iran. Methods: International and national electronic databases were searched up to June 2011 including MEDLINE, Science Citation Index Expanded, Scopus, SID, MagIran, and IranMedex as well as conference databases. Furthermore, reference lists of articles were screened and the studies' authors were contacted for additional references. Cross-sectional studies addressing the prevalence of depression among infertile couples were included in this meta-analysis. We assessed 12 separate studies involving overall 2818 participants of which 1251 had depression. Results: Overall prevalence rate of depression among infertile couples was 0.47 (95% CI: 0.40, 0.55). The prevalence rate of depression was 0.44 (95% CI: 0.32, 0.56) during 2000 to 2005 and 0.50 (95% CI: 0.43, 0.57 during 2006 to 2011. The prevalence rate of depression was 0.46 (95% CI: 0.39, 0.53) among women and 0.47 (95% CI: 0.40, 0.54) among men. Conclusion: Not only the prevalence of depression in infertile couples was high but also had increasing growth in recent years. Furthermore, despite many studies conducted addressing the prevalence of depression in infertile couples, there is however a remarkable diversity between the results. Thus, one can hardly give a precise estimation of the prevalence rate of depression among infertile couples in Iran now

Quality of Cohort Studies Reporting Post the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement

The quality of reporting of cohort studies published in the most prestigious scientific medical journals was investigated to indicate to what extent the items in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist are addressed. Six top scientific medical journals with high impact factor were selected including New England Journal of Medicine, Journal of the American Medical Association, Lancet, British Medical Journal, Archive of Internal Medicine, and Canadian Medical Association Journal. Ten cohort studies published in 2010 were selected randomly from each journal. The percentage of items in the STROBE checklist that were addressed in each study was investigated. The total percentage of items addressed by these studies was 69.3 (95% confidence interval: 59.6 to 79.0). We concluded that reporting of cohort studies published in the most prestigious scientific medical journals is not clear enough yet. The reporting of other types of observational studies such as case-control and cross-sectional studies particularly those being published in less prestigious journals expected to be much more imprecise

Identifying predictors of progression to AIDS and mortality post-HIV infection using parametric multistate model

OBJECTIVES: The human immunodeficiency virus (HIV) has already remained as a major public health problem all over the world. This study aimed to identify the prognostic factors influencing the disease progression in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) in Iran, using parametric multi-state models to take into account the intermediate event in the analysis.   METHOD(S): The data of the present retrospective cohort study was extracted in Tehran from April 2004 to March 2014. The number of 2473 HIV-infected patients in Behavioral Diseases Counseling Centers was enrolled. The outcomes of interest were the transition times from HIV diagnosis to AIDS and AIDS to death. The effect of several prognostic factors on both transitions was investigated. RESULTS: Parametric models indicated that AIDS progression was significantly associated with an increase in age (P = 0.017), low education (P = 0.026), and a decreased CD4 cell count (P = 0.001). Furthermore, the AIDS-related death was significantly associated with male sex (P = 0.010), tuberculosis coinfection (P = 0.001), antiretroviral therapy (P = 0.001) and a decreased CD4 cell count (P = 0.035). CONCLUSION: The results of this study indicated that CD4 cell count was one of the most important prognostic factors that affected and accelerated both HIV→AIDS and AIDS→DEATH transitions and antiretroviral treatment was found to be an effective measure in decelerating survival of patients with AIDS to death state. The usual Cox Model is not able to identify some of these prognostic factors.&nbsp

Prediction of Multiple sclerosis disease using machine learning classifiers: a comparative study

INTRODUCTION: Hamedan Province is one of Iran's high-risk regions for Multiple Sclerosis (MS). Early diagnosis of MS based on an accurate system can control the disease. The aim of this study was to compare the performance of four machine learning techniques with traditional methods for predicting MS patients. METHODS: The study used information regarding 200 patients through a case-control study conducted in Hamadan, Western Iran, from 2013 to 2015. The performance of six classifiers was used to compare their performance in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), negative likelihood ratio (LR-) and total accuracy. RESULTS: Random Forest (RF) model illustrated better performance among other models in both scenarios. It had greater specificity (0.67), PPV (0.68) and total accuracy (0.68). The most influential diagnostic factors for MS were age, birth season and gender. CONCLUSIONS: Our findings showed that despite all the six methods performed almost similarly, the RF model performed slightly better in terms of different criteria in prediction accuracy. Accordingly, this approach is an effective classifier for predicting MS in the early stage and control the disease

Socioeconomic status and health literacy as the important predictors of general health in Iran: a structural equation modeling approach

Background: We aimed to determine the level of health literacy (HL), and its association with general health. In addition, we investigated the direct and indirect association of socioeconomic status (SES) and general health among the adult population in Iran. Methods: This cross-sectional study involved 750 literate adults' people. The SES was assessed based on the owning of assets. HL was evaluated using a validated questionnaire in Iran. General health was assessed using the WHO general health questionnaire. The simple and adjusted linear regression models, and structural equation modeling (SEM) were used for data analysis. Results: In adjusted model, female gender, higher level of education, use of books, pamphlets, or brochures as a source of health information, the higher level of SES were positively associated with higher HL. In addition, the HL was significantly associated with higher scores of general health. Results of SEM showed that the direct effect of SES on general health was not significant, but the indirect effect via HL was significant (path coefficient: 0.24; p<0.001). Conclusion: Results of our study indicated HL is strongly associated with general health among the adult population. SES had a significant indirect association with general health via the effect on health literacy

Exploring the spatial patterns of three prevalent cancer latent risk factors in Iran; Using a shared component model

Background and aims: The aim of this study was the modeling of the incidence rates of Colorectal, breast and prostate cancers using a shared component model in order to explore the spatial pattern of their shared risk factors (i.e., obesity and low physical activity) affecting on cancer incidence, and also to estimate the relative weight of these shared components. Methods: In this study, the new cases of colorectal, breast and prostate cancers information provided by the Management Center of Ministry of Health and Medical Education in 2009 were analyzed. The Bayesian shared component model was used. In addition, BYM (Besag, York and Mollie) model was applied to investigate the geographical pattern of disease incidence rates, individually. Results: The larger effect of obesity on the incidence of the relevant cancers was found in Ardabil, West Azarbaijan, Gilan, Zanjan, Kurdistan, Qazvin, Tehran, Mazandaran, Hamadan, Kermanshah, Semnan, Golestan, Yazd and Kerman, and this component was more important for prostate cancer compared to colorectal and breast cancers. In addition, low physical activity shared component had more effect on the incidence of colorectal and breast cancers in Ardabil, Zanjan, Qazvin, Tehran, Mazandaran, Markazi, Lorestan, Kermanshah, Ilam, Khuzestan, South Khorasan, Yazd, Kerman and Fars, and also, this component was more important for Breast cancer compared to Colorectal cancer. Conclusion: Based on deviance Information criterion, combined modeling of three understudy cancers using a shared component model was better than modeling them individually using BYM model

Bayesian modeling of clustered competing risks survival times with spatial random effects

n some studies, survival data are arranged spatially such as geographical regions. Incorporating spatial associationin these data not only can increase the accuracy and efficiency of the parameter estimation, but it also investigatesthe spatial patterns of survivorship. In this paper, we considered a Bayesian hierarchical survival model in thesetting of competing risks for the spatially clustered HIV/AIDS data. In this model, a Weibull Parametric distributionwith the spatial random effects in the form of county-failure type-level was used. A multivariate intrinsic conditionalautoregressive (MCAR) distribution was employed to model the areal spatial random effects. Comparison amongcompeting models was performed by the deviance information criterion and log pseudo-marginal likelihood. Weillustrated the gains of our model through the simulation studies and application to the HIV/AIDS data

Supervised wavelet method to predict patient survival from gene expression data.

In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on combining wavelet approximation coefficients and Cox regression was presented. The proposed method was compared with supervised principal component and supervised partial least squares methods. The different fitted Cox models based on supervised wavelet approximation coefficients, the top number of supervised principal components, and partial least squares components were applied to the data. The results showed that the prediction performance of the Cox model based on supervised wavelet feature extraction was superior to the supervised principal components and partial least squares components. The results suggested the possibility of developing new tools based on wavelets for the dimensionally reduction of microarray data sets in the context of survival analysis

Application of random survival forest for competing risks in prediction of cumulative incidence function for progression to AIDS

Objective: There has remained a need to better understanding of prognostic factors that affect the survival or risk in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), particularly in developing countries. The aim of the present study aimed to identify the prognostic factors influencing AIDS progression in HIV positive patients in Hamadan province of Iran, using random survival forest in the presence of competing risks (death from causes not related to AIDS). This method considers all interactions between variables and their nonlinear effects. Method(s): A data set of 585 HIV-infected patients extracted from 1997 to 2011 was utilized. The effect of several prognostic factors on cumulative incidence function (probability) of AIDS progression and death were investigated. Result: The used model indicated that using antiretroviral therapy tuberculosis co-infection are two top most important variables in predicting cumulative incidence function for AIDS progression in the presence of competing risks, respectively. The patients with tuberculosis had much higher predicted cumulative incidence probability. Predicted cumulative incidence probability of AIDS progression was also higher for mother to child mode of HIV transmission. Moreover, transmission type and gender were two top most important variables for the competing event. Men and those patients with IDUS transmission mode had higher predicted risk compared to others. Conclusion: Considering nonlinear effects and interaction between variables, confection with tuberculosis was the most important variable in prediction of cumulative incidence probability of AIDS progression